enrichmentAnalysis.Rmd
Load data
library(easypackages)
libraries("here","pheatmap","reshape2","ggplot2","patchwork","Biobase","readxl")
source(here("code","genelistOverlap.R"))
options(stringsAsFactors = FALSE)
plspath = here("pls")
datapath = here("data","tidy")
plotdir = here("plots")
ndigits2use = 4
fdr_thresh = 0.05
fontSize = 20
dotSize = 10
mod_names = c("M1","M2","M3","M4","M5","M6","M7","M8","M9","M10",
"M11","M12","M13","M14","M15","M16","M17","M18","M19","M20","M21")
nmods = length(mod_names)
# Load in gene lists for enrichment analyses
load(here("data","tidy","enrichment_data2.Rdata"))
load(here("data","tidy","gandal_genelists.Rdata"))
load(here("data","tidy","velmeshev_genelists.Rdata"))
load(here("data","tidy","won_genelists.Rdata"))
# wgcna_res = read.csv(here("WGCNAresults","wgcna_results_summary.csv"))
wgcna_res = read_excel(here("WGCNAresults","wgcna_results_summary.xlsx"))
backgroundTotal = dim(wgcna_res)[1]
bglist = wgcna_res$geneSymbol
M0_size = dim(subset(wgcna_res, wgcna_res$moduleLabels==0))[1]
pls_fname = "plsres_all_sa_GenTemp_sexAdj_MEcorr_bootCI4plotting_LV1_ci95.csv"
fname = here("pls","results",pls_fname)
plsbootdata = read.csv(fname)
var2use = "nonzero"
td_tmp = subset(plsbootdata, plsbootdata$Grp=="TD")
rownames(td_tmp) = 1:nmods
poor_tmp = subset(plsbootdata, plsbootdata$Grp=="Poor")
rownames(poor_tmp) = 1:nmods
good_tmp = subset(plsbootdata, plsbootdata$Grp=="Good")
rownames(good_tmp) = 1:nmods
td_mods = as.numeric(rownames(td_tmp)[td_tmp[,var2use]==1])
asd_poor_mods = as.numeric(rownames(poor_tmp)[poor_tmp[,var2use]==1])
asd_good_mods = as.numeric(rownames(good_tmp)[good_tmp[,var2use]==1])
if (identical(td_mods,numeric(0))){
td_mods = NA
} else if (identical(asd_poor_mods,numeric(0))){
asd_poor_mods = NA
} else if (identical(asd_good_mods,numeric(0))){
asd_good_mods = NA
}
mask = logical(length = nmods)
nonzero_mods = sort(unique(c(td_mods, asd_poor_mods, asd_good_mods)))
mask[nonzero_mods] = TRUE
zero_mods = 1:nrow(td_tmp)
zero_mods = zero_mods[!mask]
nz_mods = mod_names[nonzero_mods]
z_mods = mod_names[zero_mods]
# non-zero and zero modules
nonzeromods = nonzero_mods
zeromods = zero_mods
# Grab non-zero modules and report percentage of genes falling within those modules
mask = is.element(wgcna_res$moduleLabels, nonzeromods)
nonzeromod_data = subset(wgcna_res, mask)
nz_genes = nonzeromod_data$geneSymbol
write(nz_genes, file = file.path(datapath,"nonzeromod_genes_SA_LV1.txt"))
# percentage of clustered genes falling within those modules
nz_prop = dim(nonzeromod_data)[1]/(backgroundTotal-M0_size)
nz_mods## [1] "M1" "M2" "M4" "M6" "M7" "M10" "M11" "M13" "M14" "M16" "M18" "M20"
## [13] "M21"nz_prop## [1] 0.6890954# Grab zero modules and report percentage of genes falling within those modules
mask = is.element(wgcna_res$moduleLabels, zeromods)
zeromod_data = subset(wgcna_res, mask)
z_genes = zeromod_data$geneSymbol
write(z_genes, file = file.path(datapath,"zeromod_genes_SA_LV1.txt"))
# percentage of clustered genes falling within those modules
z_prop = dim(zeromod_data)[1]/(backgroundTotal-M0_size)
z_mods## [1] "M3" "M5" "M8" "M9" "M12" "M15" "M17" "M19"z_prop## [1] 0.3109046Annotate each module by enrichment in broadly expressed and brain-specific genes
geneclasses = c("BroadGenesVG","BrainGenesVG")
outcols = c("OR","pval","fdr")
out_mats = vector(mode = "list", length = length(geneclasses))
names(out_mats) = geneclasses
for (igc in 1:length(geneclasses)){
out_res = data.frame(matrix(nrow = length(mod_names),
ncol = length(outcols)))
colnames(out_res) = outcols
rownames(out_res) = mod_names
# intersect genes2 list with background
genes2 = eval(as.name(geneclasses[igc]))
mask = is.element(genes2,bglist)
genes2 = data.frame(genes2[mask])
for (imod in 1:length(mod_names)){
# filename for module list
genes1 = wgcna_res$geneSymbol[wgcna_res$moduleLabels==imod]
overlap_res = genelistOverlap(genes1,
genes2,
backgroundTotal,
print_result = FALSE,
header = FALSE)
out_res[imod,1] = overlap_res[[1]]$OR
out_res[imod,2] = overlap_res[[1]]$hypergeo_p
}
out_res[,3] = p.adjust(out_res[,2], method = "fdr")
out_mats[[igc]] = out_res
}Modules enriched for broadly expressed genes
out_mats[[1]]## OR pval fdr
## M1 1.5815849 1.139045e-06 2.657771e-06
## M2 1.5316153 2.147771e-05 4.510318e-05
## M3 0.5449710 1.000000e+00 1.000000e+00
## M4 2.8610973 1.554145e-22 3.263705e-21
## M5 2.1718314 1.794960e-12 9.423540e-12
## M6 0.5160052 1.000000e+00 1.000000e+00
## M7 2.7396874 8.906490e-18 9.351815e-17
## M8 0.5593113 9.999997e-01 1.000000e+00
## M9 1.2091895 1.110790e-01 1.794352e-01
## M10 2.2929897 7.748199e-11 2.711870e-10
## M11 2.3033904 3.167846e-10 8.315596e-10
## M12 1.0797659 3.838475e-01 5.373865e-01
## M13 2.4163735 3.093854e-10 8.315596e-10
## M14 3.2033004 3.938713e-16 2.757099e-15
## M15 1.5250302 2.786712e-03 4.876746e-03
## M16 0.6472224 9.993014e-01 1.000000e+00
## M17 0.3147719 1.000000e+00 1.000000e+00
## M18 1.1586500 2.247784e-01 3.371676e-01
## M19 0.4462797 9.999993e-01 1.000000e+00
## M20 1.7165930 2.370689e-03 4.525861e-03
## M21 3.9378006 3.461351e-12 1.453767e-11Modules enriched for brain-specific genes
out_mats[[2]]## OR pval fdr
## M1 1.0068056 0.9748154711 0.999999804
## M2 1.1037777 0.3469296899 0.809502610
## M3 0.5915155 0.9999514091 0.999999804
## M4 1.5312506 0.0013649801 0.009554861
## M5 0.4254177 0.9999998042 0.999999804
## M6 0.9319914 0.7536839133 0.999999804
## M7 1.0277114 0.5250696261 0.999999804
## M8 1.6365722 0.0010703287 0.009554861
## M9 0.5636704 0.9991416678 0.999999804
## M10 0.9999960 0.5790889017 0.999999804
## M11 1.6044309 0.0030089066 0.015796760
## M12 0.3882616 0.9999939700 0.999999804
## M13 1.1628150 0.2557501163 0.671344055
## M14 0.6494483 0.9873310885 0.999999804
## M15 0.7366967 0.9475324842 0.999999804
## M16 1.5517788 0.0115805606 0.048638355
## M17 1.9033626 0.0003005418 0.006311379
## M18 1.2898588 0.1296696166 0.389008850
## M19 1.5310519 0.0267260997 0.093541349
## M20 1.0151161 0.5309440575 0.999999804
## M21 0.5123359 0.9910803412 0.999999804Plot correlation values across groups
Surface Area LV1
sa_data = read.csv(file.path(plspath,"results","plsres_all_sa_GenTemp_sexAdj_MEcorr_bootCI4plotting_LV1_ci95.csv"))
sa_good = subset(sa_data,sa_data$Grp=="Good")
sa_poor = subset(sa_data,sa_data$Grp=="Poor")
sa_td = subset(sa_data,sa_data$Grp=="TD")
#------------------------------------------------------------------------------
fontSize = 18
data4heatmap = sa_data
level_ordering = rev(c("M13","M2","M16","M21","M4","M10","M6","M7","M11","M15",
"M20","M3","M17","M5","M19","M14","M12","M9","M8","M18","M1"))
data4heatmap$ModName = factor(data4heatmap$ModName,
levels = level_ordering)
data4heatmap$Grp = factor(data4heatmap$Grp, levels = c("Poor","Good","TD"))
me_corr_comp = data.frame(matrix(nrow = dim(data4heatmap)[1], ncol = 3))
colnames(me_corr_comp) = c("Grp","gclust","allVertices")
me_corr_comp$Grp = data4heatmap$Grp
me_corr_comp$gclust = data4heatmap$corr
p = ggplot(data = data4heatmap) +
geom_tile(aes(y = ModName, x = Grp, fill= corr)) +
geom_text(aes(y= ModName, x=Grp, label = round(corr,2)),size = 5) +
scale_fill_gradientn(colors = colorRampPalette(c("blue","white","red"))(100), limits=c(-0.8,0.8)) +
# scale_fill_gradient(low = "white", high="red") +
ylab("")+xlab("") +
theme(
# Remove panel border
panel.border = element_blank(),
# Remove panel grid lines
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
# Remove panel background
panel.background = element_blank(),
axis.text.x = element_text(size=fontSize),
axis.text.y = element_text(size=fontSize),
axis.title.x = element_text(size=fontSize),
strip.text.x = element_text(size=fontSize),
axis.title.y = element_text(size=fontSize),
plot.title = element_text(hjust = 0.5, size=fontSize))
ggsave(filename = file.path(plotdir, "GCLUST_SA_sexAdj_LV1_MEcorr.pdf"), width=5,height=5)
p
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
sa_allVert_data = read.csv(file.path(plspath,"results","plsres_all_sa_allVertices_sexMeanSAAdj_MEcorr_bootCI4plotting_LV1_ci95.csv"))
# sa_good = subset(sa_data,sa_data$Grp=="Good")
# sa_poor = subset(sa_data,sa_data$Grp=="Poor")
# sa_td = subset(sa_data,sa_data$Grp=="TD")
fontSize = 18
data4heatmap = sa_allVert_data
data4heatmap$corr = data4heatmap$corr*-1
me_corr_comp$allVertices = data4heatmap$corr
level_ordering = rev(c("M13","M2","M16","M21","M4","M10","M6","M7","M11","M15",
"M20","M3","M17","M5","M19","M14","M12","M9","M8","M18","M1"))
data4heatmap$ModName = factor(data4heatmap$ModName,
levels = level_ordering)
data4heatmap$Grp = factor(data4heatmap$Grp, levels = c("Poor","Good","TD"))
p = ggplot(data = data4heatmap) +
geom_tile(aes(y = ModName, x = Grp, fill= corr)) +
geom_text(aes(y= ModName, x=Grp, label = round(corr,2)),size = 5) +
scale_fill_gradientn(colors = colorRampPalette(c("blue","white","red"))(100), limits=c(-0.8,0.8)) +
# scale_fill_gradient(low = "white", high="red") +
ylab("")+xlab("") +
theme(
# Remove panel border
panel.border = element_blank(),
# Remove panel grid lines
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
# Remove panel background
panel.background = element_blank(),
axis.text.x = element_text(size=fontSize),
axis.text.y = element_text(size=fontSize),
axis.title.x = element_text(size=fontSize),
strip.text.x = element_text(size=fontSize),
axis.title.y = element_text(size=fontSize),
plot.title = element_text(hjust = 0.5, size=fontSize))
ggsave(filename = file.path(plotdir, "AllVertices_SA_sexMeanSAAdj_LV1_MEcorr.pdf"), width=5,height=5)
p
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
grp_names = unique(me_corr_comp$Grp)
r_mat = data.frame(matrix(nrow = length(grp_names), ncol = 2))
rownames(r_mat) = grp_names
colnames(r_mat) = c("corr","pval")
tmp_res = cor.test(me_corr_comp$gclust[me_corr_comp$Grp=="Poor"],me_corr_comp$allVertices[me_corr_comp$Grp=="Poor"])
r_mat["Poor","corr"] = tmp_res$estimate
r_mat["Poor","pval"] = tmp_res$p.value
tmp_res = cor.test(me_corr_comp$gclust[me_corr_comp$Grp=="Good"],me_corr_comp$allVertices[me_corr_comp$Grp=="Good"])
r_mat["Good","corr"] = tmp_res$estimate
r_mat["Good","pval"] = tmp_res$p.value
tmp_res = cor.test(me_corr_comp$gclust[me_corr_comp$Grp=="TD"],me_corr_comp$allVertices[me_corr_comp$Grp=="TD"])
r_mat["TD","corr"] = tmp_res$estimate
r_mat["TD","pval"] = tmp_res$p.value
r_mat$Grp = rownames(r_mat)
r_mat$x_var = "corr"
p = ggplot(data = r_mat) +
geom_tile(aes(y = Grp, x = x_var, fill= corr)) +
geom_text(aes(y= Grp, x = x_var, label = round(corr,2)),size = 5) +
scale_fill_gradientn(colors = colorRampPalette(c("blue","white","red"))(100), limits=c(-1,1)) +
# scale_fill_gradient(low = "white", high="red") +
ylab("")+xlab("") +
theme(
# Remove panel border
panel.border = element_blank(),
# Remove panel grid lines
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
# Remove panel background
panel.background = element_blank(),
axis.text.x = element_text(size=fontSize),
axis.text.y = element_text(size=fontSize),
axis.title.x = element_text(size=fontSize),
strip.text.x = element_text(size=fontSize),
axis.title.y = element_text(size=fontSize),
plot.title = element_text(hjust = 0.5, size=fontSize))
ggsave(filename = file.path(plotdir, "GCLUST_AllVertices_SA_LV1_MEcorr_comp.pdf"), width=3,height=5)
r_mat## corr pval Grp x_var
## Good 0.9568105 1.202089e-11 Good corr
## Poor 0.6099266 3.326922e-03 Poor corr
## TD 0.6550045 1.270730e-03 TD corrp
#------------------------------------------------------------------------------
# Surface Area
df2plot = data.frame(Good = sa_good$corr, Poor = sa_poor$corr, TD = sa_td$corr, ModName = sa_poor$ModName)
df2plot$modtype = NA
df2plot$modtype[c(2,4,6,7,16,20,21)] = "PosNonZero"
df2plot$modtype[c(1,11,14,18)] = "NegNonZero"
# p1 = ggplot(data = df2plot, aes(x = Good, y = Poor, label=ModName))
p1 = ggplot(data = df2plot, aes(x = Good, y = Poor))
p1 = p1 + geom_point(data=df2plot, aes(fill=modtype), size=dotSize, colour="black", pch=21) +
# geom_text(colour="black") +
geom_smooth(method=lm, colour="black")
p1 = p1 + xlab("ASD Good PLS Correlation") + ylab("ASD Poor PLS Correlation") + guides(colour=FALSE, fill=FALSE) +
ggtitle("ASD Good vs ASD Poor") +
scale_fill_manual(values = c("dark blue","dark red")) +
theme(text = element_text(size=fontSize),
axis.text.x = element_text(size=fontSize),
axis.text.y = element_text(size=fontSize),
plot.title = element_text(size=fontSize,hjust=0.5))
ggsave(filename = file.path(plotdir, "plscorr_scatterplot_ASDGood_ASDPoor_SA_LV1.pdf"))
p1
cor.test(df2plot$Good, df2plot$Poor)##
## Pearson's product-moment correlation
##
## data: df2plot$Good and df2plot$Poor
## t = 1.2397, df = 19, p-value = 0.2302
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1792977 0.6307610
## sample estimates:
## cor
## 0.273563df2plot$modtype = NA
df2plot$modtype[c(2,10,13,16,21)] = "PosNonZero"
df2plot$modtype[c(1,11)] = "NegNonZero"
# p2 = ggplot(data = df2plot, aes(x = TD, y = Poor, label=ModName))
p2 = ggplot(data = df2plot, aes(x = TD, y = Poor))
p2 = p2 + geom_point(data=df2plot, aes(fill=modtype), size=dotSize, colour="black", pch=21) +
# geom_text(colour="black") +
geom_smooth(method=lm, colour="black")
p2 = p2 + xlab("TD PLS Correlation") + ylab("ASD Poor PLS Correlation") + guides(colour=FALSE, fill=FALSE) +
ggtitle("TD vs ASD Poor") +
scale_fill_manual(values = c("dark blue","dark red")) +
theme(text = element_text(size=fontSize),
axis.text.x = element_text(size=fontSize),
axis.text.y = element_text(size=fontSize),
plot.title = element_text(size=fontSize,hjust=0.5))
ggsave(filename = file.path(plotdir, "plscorr_scatterplot_TD_ASDPoor_SA_LV1.pdf"))
p2
cor.test(df2plot$TD, df2plot$Poor)##
## Pearson's product-moment correlation
##
## data: df2plot$TD and df2plot$Poor
## t = -1.9913, df = 19, p-value = 0.06102
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.71835850 0.01968846
## sample estimates:
## cor
## -0.41553df2plot$modtype = NA
df2plot$modtype[c(2,4,6,7,10,13,16,20,21)] = "PosNonZero"
df2plot$modtype[c(1,14,18)] = "NegNonZero"
# p3 = ggplot(data = df2plot, aes(x = TD, y = Good, label=ModName))
p3 = ggplot(data = df2plot, aes(x = TD, y = Good))
p3 = p3 + geom_point(data=df2plot, aes(fill=modtype), size=dotSize, colour="black", pch=21) +
# geom_text(colour="black") +
geom_smooth(method=lm, colour="black")
p3 = p3 + xlab("TD PLS Correlation") + ylab("ASD Good PLS Correlation") + guides(colour=FALSE, fill=FALSE) +
ggtitle("TD vs ASD Good") +
scale_fill_manual(values = c("dark blue","dark red")) +
theme(text = element_text(size=fontSize),
axis.text.x = element_text(size=fontSize),
axis.text.y = element_text(size=fontSize),
plot.title = element_text(size=fontSize,hjust=0.5))
ggsave(filename = file.path(plotdir, "plscorr_scatterplot_TD_ASDGood_SA_LV1.pdf"))
p3
cor.test(df2plot$TD, df2plot$Good)##
## Pearson's product-moment correlation
##
## data: df2plot$TD and df2plot$Good
## t = 2.9271, df = 19, p-value = 0.008648
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1656717 0.7972970
## sample estimates:
## cor
## 0.5574879p_final = (p1 | p2 | p3)
p_final
Examine enrichment at the gene-level between gene classes and non-zero or zero modules
geneclasses = c("BroadGenesVG","BrainGenesVG")
res_colnames = c("Non-Zero Modules","Zero Modules")
ORmat = data.frame(matrix(nrow = length(geneclasses),
ncol = length(res_colnames)))
logPmat = data.frame(matrix(nrow = length(geneclasses),
ncol = length(res_colnames)))
Pmat = data.frame(matrix(nrow = length(geneclasses),
ncol = length(res_colnames)))
FDRmat = data.frame(matrix(nrow = length(geneclasses),
ncol = length(res_colnames)))
NOverlapmat = data.frame(matrix(nrow = length(geneclasses),
ncol = length(res_colnames)))
colnames(ORmat) = res_colnames
colnames(logPmat) = res_colnames
colnames(Pmat) = res_colnames
colnames(FDRmat) = res_colnames
colnames(NOverlapmat) = res_colnames
rownames(ORmat) = geneclasses
rownames(logPmat) = geneclasses
rownames(Pmat) = geneclasses
rownames(FDRmat) = geneclasses
rownames(NOverlapmat) = geneclasses
for (i in 1:length(geneclasses)){
# intersect genes2 with background list
genes2 = eval(as.name(geneclasses[i]))
mask = is.element(genes2,bglist)
genes2 = data.frame(genes2[mask])
overlap_res = genelistOverlap(nz_genes,
genes2,
backgroundTotal,
print_result = FALSE,
header = FALSE)
ORmat[i,1] = overlap_res[[1]]$OR
logPmat[i,1] = -log10(overlap_res[[1]]$hypergeo_p)
Pmat[i,1] = overlap_res[[1]]$hypergeo_p
NOverlapmat[i,1] = overlap_res[[1]]$gene_overlap
overlap_res = genelistOverlap(z_genes,
genes2,
backgroundTotal,
print_result = FALSE,
header = FALSE)
ORmat[i,2] = overlap_res[[1]]$OR
logPmat[i,2] = -log10(overlap_res[[1]]$hypergeo_p)
Pmat[i,2] = overlap_res[[1]]$hypergeo_p
NOverlapmat[i,2] = overlap_res[[1]]$gene_overlap
}
for (i in 1:dim(Pmat)[2]){
FDRmat[,i] = p.adjust(Pmat[,i], method = "fdr")
}
zLIM = c(0,30)
par(mar = c(6, 8.5, 3, 3))
WGCNA::labeledHeatmap(Matrix = logPmat,
xLabels = colnames(ORmat),
yLabels = rownames(ORmat),
ySymbols = NULL,
colorLabels = FALSE,
colors = WGCNA::blueWhiteRed(100),
textMatrix = round(ORmat, digits = 2),
setStdMargins = FALSE,
cex.text = 3,
zlim = zLIM)
# Enrichment Odds Ratios
ORmat## Non-Zero Modules Zero Modules
## BroadGenesVG 3.482644 1.1077709
## BrainGenesVG 1.676564 0.9434521# P-values
Pmat## Non-Zero Modules Zero Modules
## BroadGenesVG 1.903419e-71 0.9999644
## BrainGenesVG 2.384893e-01 0.9999393# FDR
FDRmat## Non-Zero Modules Zero Modules
## BroadGenesVG 3.806839e-71 0.9999644
## BrainGenesVG 2.384893e-01 0.9999644# Noverlap
NOverlapmat## Non-Zero Modules Zero Modules
## BroadGenesVG 2685 898
## BrainGenesVG 767 281All enrichments
geneclasses = list(HumanSpecific_Prenatal_Zhu,
HumanSpecific_EarlyPostnatal_Zhu,
HumanSpecific_Adult_Zhu,
won_har_genes,
won_hge_fetal_genes,
won_hge_adult_genes,
won_hle_genes,
W234_PC1,
W234_PC2,
SongBirdDE,
ASDPrenatal1,
gandal_asd_down,
gandal_asd_up,
ASDCTXDownreg,
ASDCTXUpreg,
ASD102,
SFARIASD,
FMRP1,
FMRP2,
CHD81,
CHD82,
gandal_scz,
gandal_bd,
excitatory_de_genes,
inhibitory_de_genes,
microglia_de_genes,
oligodendrocyte_de_genes,
astrocyte_de_genes,
endothelial_de_genes,
vRG,oRG,PgS,PgG2M,IP,
ExN,ExM,ExMU,ExDp1,ExDp2,
InMGE,InCGE,
OPC,End,Per,Mic)
geneclassnames = c("Human-Specific Prenatal",
"Human-Specific Early Postnatal",
"Human-Specific Adult",
"Human-Accelerated Genes",
"Human-Gained Enhancers Fetal",
"Human-Gained Enhancers Adult",
"Human-Lossed Enhancers",
"PC1",
"PC2",
"Song Bird DE",
"ASD Prenatal1",
"ASD DE Downreg",
"ASD DE Upreg",
"ASD CTX Downreg",
"ASD CTX Upreg",
"ASD 102 dnPTVs",
"SFARI ASD",
"FMRP Targets1",
"FMRP Targets2",
"CHD8 Targets1",
"CHD8 Targets2",
"SCZ DE",
"BD DE",
"ASD Excitatory",
"ASD Inhibitory",
"ASD Microglia",
"ASD Oligodendrocyte",
"ASD Astrocyte",
"ASD Endothelial",
"Ventricular Radial Glia",
"Outer Radial Glia",
"Cycling Progenitors S phase",
"Cycling Progenitors G2M phase",
"Intermediate Progenitors",
"Migrating Excitatory",
"Maturing Excitatory",
"Maturing Excitatory Upper Enriched",
"Excitatory Deep Layer 1",
"Excitatory Deep Layer 2",
"Interneuron MGE",
"Interneuron CGE",
"Oligodendrocyte Precursor Cells",
"Endothelial",
"Pericyte",
"Microglia")
res_colnames = c("Non-Zero Modules","Zero Modules")
ORmat = data.frame(matrix(nrow = length(geneclasses),
ncol = length(res_colnames)))
logPmat = data.frame(matrix(nrow = length(geneclasses),
ncol = length(res_colnames)))
Pmat = data.frame(matrix(nrow = length(geneclasses),
ncol = length(res_colnames)))
FDRmat = data.frame(matrix(nrow = length(geneclasses),
ncol = length(res_colnames)))
colnames(ORmat) = res_colnames
colnames(logPmat) = res_colnames
colnames(Pmat) = res_colnames
colnames(FDRmat) = res_colnames
rownames(ORmat) = geneclassnames
rownames(logPmat) = geneclassnames
rownames(Pmat) = geneclassnames
rownames(FDRmat) = geneclassnames
tmp_cols = c("PROBE_ID","geneSymbol","moduleLabels","moduleColors","NonZeroMod",geneclassnames)
enrich_res_table = data.frame(matrix(nrow = dim(wgcna_res)[1], ncol = length(tmp_cols)))
colnames(enrich_res_table) = tmp_cols
enrich_res_table[,tmp_cols[1:4]] = wgcna_res[,tmp_cols[1:4]]
enrich_res_table[,"NonZeroMod"] = 0
enrich_res_table[is.element(enrich_res_table$moduleLabels,nonzeromods),"NonZeroMod"] = 1
for (i in 1:length(geneclasses)){
# intersect with background list
genes2 = geneclasses[[i]]
mask = is.element(genes2,bglist)
genes2 = data.frame(genes2[mask])
overlap_res = genelistOverlap(nz_genes,
genes2,
backgroundTotal,
print_result = FALSE,
header = FALSE)
ORmat[i,"Non-Zero Modules"] = overlap_res[[1]]$OR
logPmat[i,"Non-Zero Modules"] = -log10(overlap_res[[1]]$hypergeo_p)
Pmat[i,"Non-Zero Modules"] = overlap_res[[1]]$hypergeo_p
genes2export = unique(as.character(overlap_res[[1]]$overlapping_genes))
write(genes2export,
file = file.path(here("results",sprintf("%s_nonzero_overlap_SALV1.txt",geneclassnames[i]))))
mask = is.element(enrich_res_table$geneSymbol,genes2export)
enrich_res_table[, geneclassnames[i]] = 0
enrich_res_table[mask, geneclassnames[i]] = 1
# if (is.element(geneclassnames[i],c("PC1","PC2"))){
# genes2export = unique(as.character(overlap_res[[1]]$overlapping_genes))
# write(genes2export,
# file = file.path(here("results",sprintf("%s_nonzero_overlap_SALV1.txt",geneclassnames[i]))))
# }
#
# if (is.element(geneclassnames[i],c("ASD CTX Downreg","ASD Prenatal1","FMRP Targets1",
# "FMRP Targets2","CHD8 Targets1","CHD8 Targets2",
# "Ventricular Radial Glia",
# "Outer Radial Glia",
# "Cycling Progenitors S phase",
# "Cycling Progenitors G2M phase",
# "Intermediate Progenitors",
# "Maturing Excitatory","Maturing Excitatory Upper Enriched",
# "Excitatory Deep Layer 1","Interneuron MGE","Song Bird DE"))){
# genes2export = unique(as.character(overlap_res[[1]]$overlapping_genes))
# write(genes2export,
# file = file.path(here("results",sprintf("%s_nonzero_overlap_CTLV2.txt",geneclassnames[i]))))
# }
overlap_res = genelistOverlap(z_genes,
genes2,
backgroundTotal,
print_result = FALSE,
header = FALSE)
ORmat[i,"Zero Modules"] = overlap_res[[1]]$OR
logPmat[i,"Zero Modules"] = -log10(overlap_res[[1]]$hypergeo_p)
Pmat[i,"Zero Modules"] = overlap_res[[1]]$hypergeo_p
}
for (i in 1:dim(Pmat)[2]){
FDRmat[,i] = p.adjust(Pmat[,i], method = "fdr")
}
write.csv(enrich_res_table, file = here("results","enrich_res_table_SALV1.csv"))Enrichment with Song Bird DE genes
geneclassnames = c("Song Bird DE")
ORmat2use = ORmat[geneclassnames,]
Pmat2use = Pmat[geneclassnames,]
logPmat2use = logPmat[geneclassnames,]
FDRmat2use = FDRmat[geneclassnames,]
# Enrichment Odds Ratios
ORmat2use## Non-Zero Modules Zero Modules
## Song Bird DE 2.027822 1.271512# P-values
Pmat2use## Non-Zero Modules Zero Modules
## Song Bird DE 0.0001057592 0.2689112# FDR
FDRmat2use## Non-Zero Modules Zero Modules
## Song Bird DE 0.0003399403 0.7353366Enrichment with human-specific genes
geneclassnames = c("Human-Specific Prenatal",
"Human-Specific Early Postnatal",
"Human-Specific Adult",
"Human-Accelerated Genes",
"Human-Gained Enhancers Fetal",
"Human-Gained Enhancers Adult",
"Human-Lossed Enhancers")
studyname = c("Zhu et al., 2018",
"Zhu et al., 2018",
"Zhu et al., 2018",
"Won et al., 2019",
"Won et al., 2019",
"Won et al., 2019",
"Won et al., 2019")
ORmat2use = ORmat[geneclassnames,]
Pmat2use = Pmat[geneclassnames,]
logPmat2use = logPmat[geneclassnames,]
FDRmat2use = FDRmat[geneclassnames,]
# make figure
pheatmap(logPmat2use, display_numbers = round(ORmat2use,digits=2),
number_color = "black", fontsize_number = 12,
show_rownames=TRUE,
labels_col = res_colnames,
color = colorRampPalette(c('light blue','white','red'))(100),
cluster_rows = FALSE, cluster_cols = FALSE,
breaks= seq(0,-log10(0.005), length=100))
mat2use = logPmat2use
mat2use$labels = rownames(mat2use)
mat2use$cat = factor(studyname)
df4plot = melt(mat2use[,c("Non-Zero Modules","Zero Modules","labels","cat")])
mat2use = ORmat2use
mat2use$labels = rownames(mat2use)
tmp = melt(mat2use[,c("Non-Zero Modules","Zero Modules","labels")])
df4plot$OR = tmp$value
df4plot$labels = factor(df4plot$labels, levels = rev(geneclassnames))
df4plot$cat = factor(df4plot$cat, levels = rev(unique(studyname)))
p = ggplot(data = df4plot, aes(x = labels, y = value, fill=cat)) + facet_grid(. ~ variable)
p = p + geom_bar(stat="identity") +
ylab("-log10(p-value)") +
xlab(" ") +
geom_hline(yintercept = -log10(0.05)) +
coord_flip() #+
# scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100))
p
fs2use = fontSize-5
p = ggplot(data = df4plot, aes(x = labels, y = value, fill=OR)) + facet_grid(. ~ variable)
p = p + geom_bar(stat="identity", colour="black") +
ylab("-log10(p-value)") +
xlab(" ") +
geom_hline(yintercept = -log10(0.01), linetype="dashed") +
scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100),
limits = c(min(df4plot$OR),max(df4plot$OR))) +
coord_flip() +
# scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100))
theme(axis.text.x = element_text(size=fs2use),
# axis.text.y = element_text(size=fs2use),
axis.title.x = element_text(size=fs2use),
strip.text.x = element_text(size=fs2use),
plot.title = element_text(size=fs2use,hjust=0.5))
ggsave(filename = file.path(plotdir,"humanspecific_enrichment_SA_LV1.pdf"))
p
# Enrichment Odds Ratios
ORmat2use## Non-Zero Modules Zero Modules
## Human-Specific Prenatal 1.861890 1.0663144
## Human-Specific Early Postnatal 1.757550 1.0386328
## Human-Specific Adult 1.979902 1.0453592
## Human-Accelerated Genes 1.742303 1.3233811
## Human-Gained Enhancers Fetal 2.027047 0.5351976
## Human-Gained Enhancers Adult 1.773764 1.0830553
## Human-Lossed Enhancers 1.794104 0.9680589# P-values
Pmat2use## Non-Zero Modules Zero Modules
## Human-Specific Prenatal 1.937200e-03 0.9580686
## Human-Specific Early Postnatal 4.259132e-02 0.9491229
## Human-Specific Adult 1.023159e-05 0.9948661
## Human-Accelerated Genes 1.238795e-01 0.2262480
## Human-Gained Enhancers Fetal 1.340579e-01 0.9874484
## Human-Gained Enhancers Adult 7.619535e-02 0.7850607
## Human-Lossed Enhancers 5.510986e-02 0.9535322# FDR
FDRmat2use## Non-Zero Modules Zero Modules
## Human-Specific Prenatal 4.358701e-03 0.9999925
## Human-Specific Early Postnatal 6.608998e-02 0.9999925
## Human-Specific Adult 3.541705e-05 0.9999925
## Human-Accelerated Genes 1.548494e-01 0.7272256
## Human-Gained Enhancers Fetal 1.630434e-01 0.9999925
## Human-Gained Enhancers Adult 1.071497e-01 0.9999925
## Human-Lossed Enhancers 7.999819e-02 0.9999925Enrichment in prenatal gradient expression
geneclassnames = c("PC1",
"PC2")
ORmat2use = ORmat[geneclassnames,]
Pmat2use = Pmat[geneclassnames,]
logPmat2use = logPmat[geneclassnames,]
FDRmat2use = FDRmat[geneclassnames,]
# make figure
pheatmap(logPmat2use, display_numbers = round(ORmat2use,digits=2),
number_color = "black", fontsize_number = fontSize,
show_rownames=TRUE,
labels_col = res_colnames,
color = colorRampPalette(c('light blue','white','red'))(100),
cluster_rows = FALSE, cluster_cols = FALSE,
fontsize_row = fontSize, fontsize_col = fontSize,
breaks= seq(0,-log10(0.005), length=100))
mat2use = logPmat2use
mat2use$labels = rownames(mat2use)
df4plot = melt(mat2use[,c("Non-Zero Modules","Zero Modules","labels")])
mat2use = ORmat2use
mat2use$labels = rownames(mat2use)
tmp = melt(mat2use[,c("Non-Zero Modules","Zero Modules","labels")])
df4plot$OR = tmp$value
df4plot$labels = factor(df4plot$labels, levels = rev(geneclassnames))
p = ggplot(data = df4plot, aes(x = labels, y = value)) + facet_grid(. ~ variable)
p = p + geom_bar(stat="identity") +
ylab("-log10(p-value)") +
xlab(" ") +
geom_hline(yintercept = -log10(0.05)) +
coord_flip() #+
# scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100))
p = p + theme(text = element_text(size=fontSize),
axis.text.x = element_text(size=fontSize),
axis.text.y = element_text(size=fontSize))
p
# Enrichment Odds Ratios
ORmat2use## Non-Zero Modules Zero Modules
## PC1 2.695846 0.9971211
## PC2 2.869637 1.0189664# P-values
Pmat2use## Non-Zero Modules Zero Modules
## PC1 1.264299e-33 0.9999895
## PC2 7.117349e-43 0.9999925# FDR
FDRmat2use## Non-Zero Modules Zero Modules
## PC1 1.896448e-32 0.9999925
## PC2 3.202807e-41 0.9999925Examine enrichment between non-zero or zero modules and song bird DE, Human Specific, ASD Prenatal, ASD CTX Dysregulated Modules, ASD PTVs, ASD SFARI, and FMRP and CHD8 targets
geneclassnames = c("ASD 102 dnPTVs",
"SFARI ASD",
"ASD DE Downreg",
"ASD DE Upreg",
"ASD CTX Downreg",
"ASD CTX Upreg",
"ASD Prenatal1",
"FMRP Targets1",
"FMRP Targets2",
"CHD8 Targets1",
"CHD8 Targets2",
"SCZ DE",
"BD DE")
ORmat2use = ORmat[geneclassnames,]
Pmat2use = Pmat[geneclassnames,]
logPmat2use = logPmat[geneclassnames,]
FDRmat2use = FDRmat[geneclassnames,]
# make figure
pheatmap(logPmat2use, display_numbers = round(ORmat2use,digits=2),
number_color = "black", fontsize_number = 12,
show_rownames=TRUE,
labels_col = res_colnames,
color = colorRampPalette(c('light blue','white','red'))(100),
cluster_rows = FALSE, cluster_cols = FALSE,
breaks= seq(0,-log10(0.005), length=100))
mat2use = logPmat2use
mat2use$labels = rownames(mat2use)
df4plot = melt(mat2use[,c("Non-Zero Modules","Zero Modules","labels")])
mat2use = ORmat2use
mat2use$labels = rownames(mat2use)
tmp = melt(mat2use[,c("Non-Zero Modules","Zero Modules","labels")])
df4plot$OR = tmp$value
df4plot$labels = factor(df4plot$labels, levels = rev(geneclassnames))
p = ggplot(data = df4plot, aes(x = labels, y = value)) + facet_grid(. ~ variable)
p = p + geom_bar(stat="identity") +
ylab("-log10(p-value)") +
xlab(" ") +
geom_hline(yintercept = -log10(0.05)) +
coord_flip() #+
# scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100))
p
fs2use = fontSize-5
p = ggplot(data = df4plot, aes(x = labels, y = value, fill=OR)) + facet_grid(. ~ variable)
p = p + geom_bar(stat="identity", colour="black") +
ylab("-log10(p-value)") +
xlab(" ") +
geom_hline(yintercept = -log10(0.01), linetype="dashed") +
scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100),
limits = c(min(df4plot$OR),max(df4plot$OR))) +
ylim(0,42) +
coord_flip() +
# scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100))
theme(axis.text.x = element_text(size=fs2use),
# axis.text.y = element_text(size=fs2use),
axis.title.x = element_text(size=fs2use),
strip.text.x = element_text(size=fs2use),
plot.title = element_text(size=fs2use,hjust=0.5))
ggsave(filename = file.path(plotdir,"asdgenelist_enrichment_SA_LV1.pdf"))
p
# Enrichment Odds Ratios
ORmat2use## Non-Zero Modules Zero Modules
## ASD 102 dnPTVs 1.941935 0.8773216
## SFARI ASD 1.802044 1.0035060
## ASD DE Downreg 1.891583 1.1120901
## ASD DE Upreg 2.177891 1.7506987
## ASD CTX Downreg 1.988391 0.9071381
## ASD CTX Upreg 1.846795 1.7275377
## ASD Prenatal1 2.367159 0.8666482
## FMRP Targets1 2.437915 0.7347917
## FMRP Targets2 2.354752 1.4096707
## CHD8 Targets1 2.501138 1.1002091
## CHD8 Targets2 2.857467 1.0443434
## SCZ DE 2.157613 1.2516533
## BD DE 1.630222 1.3250078# P-values
Pmat2use## Non-Zero Modules Zero Modules
## ASD 102 dnPTVs 1.845038e-01 8.436208e-01
## SFARI ASD 4.541149e-02 9.268890e-01
## ASD DE Downreg 2.074753e-02 7.122944e-01
## ASD DE Upreg 1.260561e-04 5.338003e-04
## ASD CTX Downreg 2.876274e-03 9.850967e-01
## ASD CTX Upreg 1.433097e-02 7.894146e-05
## ASD Prenatal1 2.407864e-12 9.999503e-01
## FMRP Targets1 2.689136e-07 9.998277e-01
## FMRP Targets2 1.221707e-03 1.898297e-01
## CHD8 Targets1 4.762385e-21 9.454243e-01
## CHD8 Targets2 9.966104e-42 9.999674e-01
## SCZ DE 1.950031e-10 3.727167e-01
## BD DE 2.866176e-01 1.791875e-01# FDR
FDRmat2use## Non-Zero Modules Zero Modules
## ASD 102 dnPTVs 2.128890e-01 0.999992481
## SFARI ASD 6.811724e-02 0.999992481
## ASD DE Downreg 3.590919e-02 0.999992481
## ASD DE Upreg 3.781684e-04 0.008007004
## ASD CTX Downreg 5.627492e-03 0.999992481
## ASD CTX Upreg 2.579574e-02 0.003552366
## ASD Prenatal1 1.354423e-11 0.999992481
## FMRP Targets1 1.100101e-06 0.999992481
## FMRP Targets2 3.233932e-03 0.657102901
## CHD8 Targets1 4.286147e-20 0.999992481
## CHD8 Targets2 2.242373e-40 0.999992481
## SCZ DE 9.750153e-10 0.840000727
## BD DE 3.070903e-01 0.657102901Examine enrichment between ASD DE cell-type lists (Velmeshev et al.,)
geneclassnames = c("ASD Excitatory",
"ASD Inhibitory",
"ASD Microglia",
"ASD Oligodendrocyte",
"ASD Astrocyte",
"ASD Endothelial")
ORmat2use = ORmat[geneclassnames,]
Pmat2use = Pmat[geneclassnames,]
logPmat2use = logPmat[geneclassnames,]
FDRmat2use = FDRmat[geneclassnames,]
# make figure
pheatmap(logPmat2use, display_numbers = round(ORmat2use,digits=2),
number_color = "black", fontsize_number = 12,
show_rownames=TRUE,
labels_col = res_colnames,
color = colorRampPalette(c('light blue','white','red'))(100),
cluster_rows = FALSE, cluster_cols = FALSE,
breaks= seq(0,-log10(0.005), length=100))
mat2use = logPmat2use
mat2use$labels = rownames(mat2use)
df4plot = melt(mat2use[,c("Non-Zero Modules","Zero Modules","labels")])
mat2use = ORmat2use
mat2use$labels = rownames(mat2use)
tmp = melt(mat2use[,c("Non-Zero Modules","Zero Modules","labels")])
df4plot$OR = tmp$value
fs2use = fontSize-5
df4plot$labels = factor(df4plot$labels, levels = rev(geneclassnames))
p = ggplot(data = df4plot, aes(x = labels, y = value, fill=OR)) + facet_grid(. ~ variable)
p = p + geom_bar(stat="identity", colour="black") +
ylab("-log10(p-value)") +
xlab(" ") +
geom_hline(yintercept = -log10(0.01), linetype="dashed") +
scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100),
limits = c(min(df4plot$OR),max(df4plot$OR))) +
ylim(0,3) +
coord_flip() +
# scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100))
theme(axis.text.x = element_text(size=fs2use),
# axis.text.y = element_text(size=fs2use),
axis.title.x = element_text(size=fs2use),
strip.text.x = element_text(size=fs2use),
plot.title = element_text(size=fs2use,hjust=0.5))
ggsave(filename = file.path(plotdir,"asdcelltypes_enrichment_SA_LV1.pdf"))
p
# Enrichment Odds Ratios
ORmat2use## Non-Zero Modules Zero Modules
## ASD Excitatory 1.0596125 0.7413811
## ASD Inhibitory 1.4548400 1.3434037
## ASD Microglia 3.3654820 0.7718658
## ASD Oligodendrocyte 0.8712329 2.8643521
## ASD Astrocyte 2.0359782 0.6224596
## ASD Endothelial 2.2485583 2.2252276# P-values
Pmat2use## Non-Zero Modules Zero Modules
## ASD Excitatory 0.970056106 0.95943703
## ASD Inhibitory 0.624961438 0.37333366
## ASD Microglia 0.002480906 0.88608692
## ASD Oligodendrocyte 0.878642200 0.13015603
## ASD Astrocyte 0.255198306 0.91593966
## ASD Endothelial 0.150090171 0.07017566# FDR
FDRmat2use## Non-Zero Modules Zero Modules
## ASD Excitatory 0.970056106 0.9999925
## ASD Inhibitory 0.654029412 0.8400007
## ASD Microglia 0.005316226 0.9999925
## ASD Oligodendrocyte 0.898611341 0.5324565
## ASD Astrocyte 0.280095702 0.9999925
## ASD Endothelial 0.177738361 0.4144282Examine enrichment between prenatal cell-type lists
geneclassnames = c("Ventricular Radial Glia",
"Outer Radial Glia",
"Cycling Progenitors S phase",
"Cycling Progenitors G2M phase",
"Intermediate Progenitors",
"Migrating Excitatory",
"Maturing Excitatory",
"Maturing Excitatory Upper Enriched",
"Excitatory Deep Layer 1",
"Excitatory Deep Layer 2",
"Interneuron MGE",
"Interneuron CGE",
"Oligodendrocyte Precursor Cells",
"Endothelial",
"Pericyte",
"Microglia")
genetypecat = c("Progenitor",
"Progenitor",
"Progenitor",
"Progenitor",
"Progenitor",
"Excitatory",
"Excitatory",
"Excitatory",
"Excitatory",
"Excitatory",
"Inhibitory",
"Inhibitory",
"Other",
"Other",
"Other",
"Other")
ORmat2use = ORmat[geneclassnames,]
Pmat2use = Pmat[geneclassnames,]
logPmat2use = logPmat[geneclassnames,]
FDRmat2use = FDRmat[geneclassnames,]
# make figure
pheatmap(logPmat2use, display_numbers = round(ORmat2use,digits=2),
number_color = "black", fontsize_number = 12,
show_rownames=TRUE,
labels_col = res_colnames,
color = colorRampPalette(c('light blue','white','red'))(100),
cluster_rows = FALSE, cluster_cols = FALSE,
breaks= seq(0,-log10(0.005), length=100))
# make bar plot
mat2use = logPmat2use
mat2use$labels = rownames(mat2use)
mat2use$cat = genetypecat
mat2use$labels = factor(mat2use$labels, levels = rev(geneclassnames))
mat2use$cat = factor(mat2use$cat, levels = rev(unique(genetypecat)))
df4plot = melt(mat2use[,c("Non-Zero Modules","Zero Modules","labels","cat")])
mat2use = ORmat2use
mat2use$labels = rownames(mat2use)
tmp = melt(mat2use[,c("Non-Zero Modules","Zero Modules","labels")])
df4plot$OR = tmp$value
p = ggplot(data = df4plot, aes(x = labels, y = value, fill=cat)) + facet_grid(. ~ variable)
p = p + geom_bar(stat="identity") +
ylab("-log10(p-value)") +
xlab(" ") +
geom_hline(yintercept = -log10(0.01)) +
coord_flip() #+
# scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100))
p
fs2use = fontSize-5
p = ggplot(data = df4plot, aes(x = labels, y = value, fill=OR)) + facet_grid(. ~ variable)
p = p + geom_bar(stat="identity", colour="black") +
ylab("-log10(p-value)") +
xlab(" ") + ggtitle("SA LV1") +
geom_hline(yintercept = -log10(0.01), linetype = "dashed") +
scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100),
limits = c(min(df4plot$OR),max(df4plot$OR))) +
ylim(0,30) +
coord_flip() +
# theme(text = element_text(size=fs2use),
theme(axis.text.x = element_text(size=fs2use),
# axis.text.y = element_text(size=fs2use),
axis.title.x = element_text(size=fs2use),
strip.text.x = element_text(size=fs2use),
plot.title = element_text(size=fs2use,hjust=0.5))
# scale_fill_gradientn(colors = colorRampPalette(c("white","red"))(100))
ggsave(filename = file.path(plotdir, "prenatalCellTypes_enrichments_SA_LV1.pdf"))
p
# Enrichment Odds Ratios
ORmat2use## Non-Zero Modules Zero Modules
## Ventricular Radial Glia 2.108539 1.6102882
## Outer Radial Glia 2.147758 1.7041182
## Cycling Progenitors S phase 3.720220 1.0415594
## Cycling Progenitors G2M phase 3.932447 0.9565357
## Intermediate Progenitors 2.392155 1.3337578
## Migrating Excitatory 1.940031 1.0480835
## Maturing Excitatory 2.355677 1.3762499
## Maturing Excitatory Upper Enriched 2.192684 1.1372814
## Excitatory Deep Layer 1 1.828289 1.4746735
## Excitatory Deep Layer 2 1.882035 1.3958769
## Interneuron MGE 2.061853 1.4339981
## Interneuron CGE 2.011156 0.8976807
## Oligodendrocyte Precursor Cells 2.681489 0.9086132
## Endothelial 2.075416 1.7245064
## Pericyte 2.482869 1.7283126
## Microglia 3.457488 1.3997582# P-values
Pmat2use## Non-Zero Modules Zero Modules
## Ventricular Radial Glia 2.775231e-03 0.0181985676
## Outer Radial Glia 5.937467e-04 0.0026542473
## Cycling Progenitors S phase 1.540236e-20 0.8514490206
## Cycling Progenitors G2M phase 1.149742e-18 0.9293938050
## Intermediate Progenitors 1.792397e-03 0.3033142231
## Migrating Excitatory 2.454356e-01 0.6772099027
## Maturing Excitatory 6.377631e-03 0.2777938153
## Maturing Excitatory Upper Enriched 2.297073e-02 0.6056498484
## Excitatory Deep Layer 1 8.350139e-02 0.0900410065
## Excitatory Deep Layer 2 2.451093e-02 0.1157974923
## Interneuron MGE 8.795831e-02 0.2706437352
## Interneuron CGE 1.105532e-01 0.8523472608
## Oligodendrocyte Precursor Cells 3.559114e-07 0.9580774285
## Endothelial 1.870407e-03 0.0019157088
## Pericyte 4.535565e-09 0.0001875457
## Microglia 2.019680e-23 0.0736761231# FDR
FDRmat2use## Non-Zero Modules Zero Modules
## Ventricular Radial Glia 5.627492e-03 0.136489257
## Outer Radial Glia 1.669913e-03 0.023888226
## Cycling Progenitors S phase 1.155177e-19 0.999992481
## Cycling Progenitors G2M phase 7.391198e-18 0.999992481
## Intermediate Progenitors 4.358701e-03 0.758285558
## Migrating Excitatory 2.761150e-01 0.999992481
## Maturing Excitatory 1.195806e-02 0.735336570
## Maturing Excitatory Upper Enriched 3.828456e-02 0.999992481
## Excitatory Deep Layer 1 1.138655e-01 0.450205033
## Excitatory Deep Layer 2 3.939256e-02 0.521088715
## Interneuron MGE 1.164154e-01 0.735336570
## Interneuron CGE 1.421399e-01 0.999992481
## Oligodendrocyte Precursor Cells 1.334668e-06 0.999992481
## Endothelial 4.358701e-03 0.021551724
## Pericyte 2.041004e-08 0.004219778
## Microglia 2.272140e-22 0.414428192Assess enrichments with individual modules
geneclasses = list(HumanSpecific_Prenatal_Zhu,
HumanSpecific_EarlyPostnatal_Zhu,
HumanSpecific_Adult_Zhu,
won_har_genes,
won_hge_fetal_genes,
won_hge_adult_genes,
won_hle_genes,
W234_PC1,
W234_PC2,
SongBirdDE,
ASDPrenatal1,
gandal_asd_down,
gandal_asd_up,
ASDCTXDownreg,
ASDCTXUpreg,
ASD102,
SFARIASD,
FMRP1,
FMRP2,
CHD81,
CHD82,
gandal_scz,
gandal_bd,
excitatory_de_genes,
inhibitory_de_genes,
microglia_de_genes,
oligodendrocyte_de_genes,
astrocyte_de_genes,
endothelial_de_genes,
vRG,oRG,PgS,PgG2M,IP,
ExN,ExM,ExMU,ExDp1,ExDp2,
InMGE,InCGE,
OPC,End,Per,Mic)
geneclassnames = c("Human-Specific Prenatal",
"Human-Specific Early Postnatal",
"Human-Specific Adult",
"Human-Accelerated Genes",
"Human-Gained Enhancers Fetal",
"Human-Gained Enhancers Adult",
"Human-Lossed Enhancers",
"PC1",
"PC2",
"Song Bird DE",
"ASD Prenatal1",
"ASD DE Downreg",
"ASD DE Upreg",
"ASD CTX Downreg",
"ASD CTX Upreg",
"ASD 102 dnPTVs",
"SFARI ASD",
"FMRP Targets1",
"FMRP Targets2",
"CHD8 Targets1",
"CHD8 Targets2",
"SCZ DE",
"BD DE",
"ASD Excitatory",
"ASD Inhibitory",
"ASD Microglia",
"ASD Oligodendrocyte",
"ASD Astrocyte",
"ASD Endothelial",
"Ventricular Radial Glia",
"Outer Radial Glia",
"Cycling Progenitors S phase",
"Cycling Progenitors G2M phase",
"Intermediate Progenitors",
"Migrating Excitatory",
"Maturing Excitatory",
"Maturing Excitatory Upper Enriched",
"Excitatory Deep Layer 1",
"Excitatory Deep Layer 2",
"Interneuron MGE",
"Interneuron CGE",
"Oligodendrocyte Precursor Cells",
"Endothelial",
"Pericyte",
"Microglia")
ORmat = data.frame(matrix(nrow = length(mod_names),
ncol = length(geneclasses)))
colnames(ORmat) = geneclassnames
rownames(ORmat) = mod_names
Pmat = data.frame(matrix(nrow = length(mod_names),
ncol = length(geneclasses)))
colnames(Pmat) = geneclassnames
rownames(Pmat) = mod_names
FDRmat = data.frame(matrix(nrow = length(mod_names),
ncol = length(geneclasses)))
colnames(FDRmat) = geneclassnames
rownames(FDRmat) = mod_names
for (imod in 1:length(mod_names)){
for (igc in 1:length(geneclasses)){
# intersect geneclass list with background
genes2 = geneclasses[[igc]]
mask = is.element(genes2,bglist)
genes2 = data.frame(genes2[mask])
modulegenes = wgcna_res$geneSymbol[wgcna_res$moduleLabels==imod]
overlap_res = genelistOverlap(modulegenes,
genes2,
backgroundTotal,
print_result = FALSE,
header = FALSE)
ORmat[imod,igc] = overlap_res[[1]]$OR
Pmat[imod,igc] = overlap_res[[1]]$hypergeo_p
}
}
for (i in 1:dim(Pmat)[2]){
FDRmat[,i] = p.adjust(Pmat[,i], method = "fdr")
}
# Enrichment Odds Ratios
ORmat## Human-Specific Prenatal Human-Specific Early Postnatal Human-Specific Adult
## M1 1.1132368 1.1238591 1.1344044
## M2 1.1777173 1.2799489 1.3759725
## M3 1.0706029 1.2620357 0.8883251
## M4 1.5367836 1.2279924 1.5517923
## M5 0.6323972 0.6472886 0.6335012
## M6 1.5199793 1.8844683 1.1425708
## M7 1.3175099 1.1496226 1.3053771
## M8 1.1105825 1.1887275 0.8597516
## M9 1.1750976 0.9257732 1.4040835
## M10 0.4456115 0.5673747 1.0180548
## M11 1.2196126 1.2008985 1.3800185
## M12 0.4616028 0.3063472 0.6067416
## M13 1.8676503 1.5086759 1.4563663
## M14 0.7206041 0.2251307 0.7997053
## M15 0.8233101 0.4948072 0.8033923
## M16 0.7600700 0.6488067 0.8694325
## M17 1.0802160 1.2399662 1.1311926
## M18 1.4506673 1.8609779 1.4318157
## M19 1.1441588 1.1358959 1.1893837
## M20 1.3070363 1.7072932 1.1954910
## M21 1.4626123 0.5388963 1.4574988
## Human-Accelerated Genes Human-Gained Enhancers Fetal
## M1 1.0434343 1.3786244
## M2 0.6818672 0.6804173
## M3 1.0980310 0.0000000
## M4 1.1110284 2.4213847
## M5 1.5743486 0.4815262
## M6 1.1420231 1.7186826
## M7 0.9240883 0.5546150
## M8 1.3296411 1.3066837
## M9 0.6130077 0.0000000
## M10 1.4049038 0.0000000
## M11 1.8424555 1.5045083
## M12 0.6833528 0.7514139
## M13 1.5702998 2.5857658
## M14 1.9205537 2.5974661
## M15 1.3371035 0.0000000
## M16 1.3853495 0.0000000
## M17 1.0960523 2.0194202
## M18 1.5716830 1.0469138
## M19 1.1042258 0.0000000
## M20 0.8160205 1.5028278
## M21 0.5610851 0.0000000
## Human-Gained Enhancers Adult Human-Lossed Enhancers PC1 PC2
## M1 1.4005863 1.2081383 1.2965280 1.3312689
## M2 0.9068488 0.8452595 1.6709264 1.5917948
## M3 0.9212996 0.5028175 0.4254575 0.4455956
## M4 1.0093463 1.1584775 1.9863912 2.3242251
## M5 0.9901311 0.7741005 1.2064919 1.2257953
## M6 0.9596594 1.2457597 0.7439276 0.7928571
## M7 0.9596594 0.4761992 1.4811842 1.8958460
## M8 0.6951303 2.1051448 1.2403581 1.1429745
## M9 1.6421638 0.7843513 0.6320782 0.7190808
## M10 1.5294293 1.6489699 2.0354924 2.1218871
## M11 0.6836462 1.9340685 1.2438291 1.5713825
## M12 0.3410716 0.4272263 0.8150297 0.9369811
## M13 1.6056914 1.7708381 2.0922910 1.5158283
## M14 1.4687746 1.2372942 2.1884845 1.9808598
## M15 0.9773119 0.9121475 1.1874750 1.2105523
## M16 1.3206748 1.6789373 1.2169196 1.0167401
## M17 0.6059710 1.0123834 0.7431452 0.6516781
## M18 1.1477733 1.2338626 1.2414780 1.3756739
## M19 1.5276575 0.5120890 0.9859597 0.9255865
## M20 0.4538182 0.2097982 1.5554365 1.7997589
## M21 0.7171249 0.4417909 1.8455701 2.2184071
## Song Bird DE ASD Prenatal1 ASD DE Downreg ASD DE Upreg ASD CTX Downreg
## M1 1.3375342 1.2665346 0.8239400 2.2553876 1.0472613
## M2 0.9105077 0.6434830 1.5258927 0.8537522 1.0477397
## M3 0.9493592 1.1722718 1.0185873 0.4173641 0.6707316
## M4 0.9500158 0.1228903 1.8233211 0.3473206 1.7459433
## M5 1.5392690 0.3763681 1.3671554 0.5974917 0.9124949
## M6 0.7562689 0.2432538 0.7586843 1.0568188 0.5518329
## M7 1.4511140 0.2120242 1.9126333 0.4247354 1.4094346
## M8 1.2498427 0.2827933 0.7672684 1.6795633 1.0300270
## M9 0.5642168 0.4147352 0.2238206 7.0409095 0.4763636
## M10 1.5970249 9.0323937 1.0541437 0.8471897 3.3208439
## M11 0.8866549 0.7260928 1.1535097 0.4521034 1.1917667
## M12 0.6970902 0.5082274 0.3761643 0.2269324 0.2086184
## M13 1.8291349 2.9622875 1.4579205 1.3256290 1.7499213
## M14 2.5780061 7.0728335 0.5621078 2.2205765 0.9653950
## M15 1.2578359 2.8326377 1.7454432 1.1295863 1.4566038
## M16 0.9537124 0.9869081 1.2866348 1.4844283 0.9338711
## M17 1.2109172 0.6805078 1.3790450 0.9266119 1.0005762
## M18 0.5200005 0.5929365 1.0771484 2.0566833 0.5911785
## M19 1.2583705 0.4695838 1.0260495 1.3305233 1.0385794
## M20 1.6035872 1.5866473 2.1333944 0.9303615 1.7822678
## M21 2.3342532 2.2724183 2.5356430 1.4874560 1.1267034
## ASD CTX Upreg ASD 102 dnPTVs SFARI ASD FMRP Targets1 FMRP Targets2
## M1 2.4256767 1.4435490 1.3002438 1.56260898 1.6157005
## M2 0.5597165 0.3488236 0.4518244 0.19382106 0.4803705
## M3 0.3157409 0.4032140 0.9154303 0.81791849 1.4482154
## M4 0.3236766 0.4730389 0.3634480 0.13050885 0.1597055
## M5 1.0345713 0.5011523 0.5858083 0.27912251 0.6917835
## M6 0.7560540 0.0000000 0.9971901 0.00000000 1.4290536
## M7 0.2805303 0.0000000 0.3687896 0.07927386 0.3927584
## M8 2.1351231 0.6687791 0.7873098 0.86381323 2.1709763
## M9 4.3848560 0.7027027 0.4501403 0.19410225 0.7236972
## M10 1.0083486 3.7838693 2.8866215 7.98794515 1.7531140
## M11 0.3755486 0.0000000 0.3926535 1.35589642 1.0668468
## M12 0.3817051 0.0000000 0.5017957 0.32612570 1.0842647
## M13 0.5998410 0.0000000 0.9077768 1.95697413 2.8343301
## M14 0.9703111 5.7267651 4.4600323 6.07257092 5.4166032
## M15 1.0438195 5.0266106 2.2170578 1.50976562 1.6354828
## M16 1.5158768 1.9661644 1.2872289 0.96770026 0.9964474
## M17 1.3918266 0.0000000 1.2334520 0.88207502 0.7036047
## M18 2.5338198 4.6213280 1.6212372 1.60478448 1.5179706
## M19 2.3179896 0.0000000 0.9813097 0.88905367 0.8543771
## M20 0.9405353 0.0000000 0.5999480 0.65759773 1.6253526
## M21 0.6413981 0.0000000 2.0001924 0.68570020 1.1150345
## CHD8 Targets1 CHD8 Targets2 SCZ DE BD DE ASD Excitatory
## M1 1.3221433 0.9735876 1.7161930 1.3916275 0.6191434
## M2 1.1831294 1.7206456 1.0314357 1.7019737 1.6717098
## M3 0.6947457 0.6679055 0.6212525 0.6172295 0.5230994
## M4 0.9456434 2.2586003 0.7769991 0.6103055 0.6139064
## M5 1.6052584 2.0562018 0.9331181 0.4648758 0.0000000
## M6 0.7089355 1.5247056 0.9652261 0.6775443 0.0000000
## M7 1.3000209 3.5344508 0.7360237 0.5369386 0.7495156
## M8 0.6384245 0.3800321 1.2586838 1.2142958 1.7812344
## M9 0.6491459 0.5801498 1.9612071 2.0509100 0.4508822
## M10 2.7294098 1.8954317 1.4622242 0.6591522 0.4525217
## M11 1.7158066 1.7318974 0.9838642 1.4081749 1.0003495
## M12 1.1675201 1.3610393 0.6252518 1.2266129 0.0000000
## M13 1.9583677 2.2816417 1.6582297 1.2524098 0.0000000
## M14 4.6528966 2.9221239 1.0812531 0.6057241 0.5580425
## M15 2.0105699 2.1125080 0.8860674 0.8788697 1.2129842
## M16 0.6759569 0.4151154 1.4987981 1.0314368 1.9095033
## M17 0.5098605 0.3383445 1.3829212 1.9261243 1.3427151
## M18 0.6170034 0.4986443 1.8683426 1.3109881 2.9212245
## M19 0.4171047 0.2248669 1.5748273 1.8525228 0.8028612
## M20 1.8327176 2.1116026 1.5136179 0.5384856 1.0035764
## M21 2.6872466 3.4649912 1.1539066 1.1583144 0.0000000
## ASD Inhibitory ASD Microglia ASD Oligodendrocyte ASD Astrocyte
## M1 0.7323658 2.5458717 0.6983527 0.8108641
## M2 1.9284802 0.9590924 2.1603717 0.0000000
## M3 0.7029902 0.0000000 0.0000000 0.9067599
## M4 0.8250252 1.3014579 0.0000000 0.0000000
## M5 1.3304267 0.0000000 0.0000000 0.0000000
## M6 0.0000000 0.0000000 0.0000000 0.0000000
## M7 0.4962625 0.0000000 0.0000000 0.0000000
## M8 1.1675962 0.9010031 0.0000000 0.0000000
## M9 0.6041456 0.9467062 0.0000000 0.0000000
## M10 1.8762428 1.9426872 0.0000000 1.5860055
## M11 0.6616833 1.0368687 0.0000000 5.5722581
## M12 0.6723556 2.1550457 4.8434140 0.0000000
## M13 0.7443937 1.1664773 0.0000000 0.0000000
## M14 0.7477318 5.0258741 0.0000000 4.0523649
## M15 3.3718484 0.0000000 0.0000000 2.0968823
## M16 0.8295731 0.0000000 0.0000000 4.4981250
## M17 1.8044668 0.0000000 14.0155566 2.3199549
## M18 3.9498795 4.6225343 0.0000000 5.0826271
## M19 0.0000000 3.4564695 7.7494624 0.0000000
## M20 1.3447112 2.1071848 0.0000000 0.0000000
## M21 1.4012116 2.1957219 10.0938375 3.6651388
## ASD Endothelial Ventricular Radial Glia Outer Radial Glia
## M1 3.7446200 1.1413671 1.2956781
## M2 0.0000000 1.8069118 0.6887162
## M3 0.7871758 0.6179825 0.4987745
## M4 0.0000000 0.6193648 0.7615201
## M5 0.9783762 1.3590345 1.4890216
## M6 1.1268797 2.2977377 2.5817303
## M7 0.0000000 1.8571899 2.3302690
## M8 4.1637731 1.5060968 2.0142402
## M9 2.8283047 1.7555259 1.6918635
## M10 2.8386269 1.2556026 1.4190152
## M11 0.0000000 0.3291234 0.5396743
## M12 3.1489222 1.2129821 1.2755461
## M13 0.0000000 0.9472835 0.7654065
## M14 1.6978995 0.7555377 1.2566586
## M15 1.8203441 1.2356785 0.9978752
## M16 0.0000000 1.5053419 0.6786813
## M17 0.0000000 1.6128663 1.7007282
## M18 2.1271437 1.1977148 2.2383275
## M19 0.0000000 2.2736587 2.5933726
## M20 0.0000000 3.2734875 2.9651962
## M21 0.0000000 1.0622926 1.1584388
## Cycling Progenitors S phase Cycling Progenitors G2M phase
## M1 0.3935776 0.8067843
## M2 1.7145908 0.5030133
## M3 1.0812357 0.4967516
## M4 1.9899011 1.2060065
## M5 0.9957732 1.1666114
## M6 15.9304653 19.9834244
## M7 3.9496646 3.9725873
## M8 0.3184035 0.5461163
## M9 0.4488786 0.7223546
## M10 1.2921545 0.8757547
## M11 0.2430105 0.1541700
## M12 1.7226614 1.6680693
## M13 1.7577968 1.4612579
## M14 0.4149331 0.3510020
## M15 0.9096157 0.7645628
## M16 0.3049782 0.1932877
## M17 0.6623992 0.8472182
## M18 0.3446081 0.6660038
## M19 1.2310132 0.7661220
## M20 1.5516973 1.6373371
## M21 2.5124348 2.4482947
## Intermediate Progenitors Migrating Excitatory Maturing Excitatory
## M1 0.7286526 1.1435462 1.3133818
## M2 4.9598677 0.5277125 2.7498969
## M3 1.5230539 0.6099962 0.0000000
## M4 0.5691336 0.7156297 0.7241886
## M5 1.6756248 1.5520294 0.7674519
## M6 0.9349021 0.0000000 1.1913580
## M7 2.7319785 1.7883085 0.8846376
## M8 0.5327223 0.0000000 2.1177168
## M9 0.0000000 0.0000000 0.3519422
## M10 1.7489257 3.3619359 1.0823039
## M11 0.3038061 0.0000000 0.0000000
## M12 1.2707037 1.1830974 0.3916776
## M13 0.0000000 1.3098579 0.8772038
## M14 0.6934989 0.0000000 1.8032828
## M15 0.0000000 1.4106162 3.5108138
## M16 0.3808912 8.1111111 3.0793331
## M17 1.6828238 1.5606817 3.2980836
## M18 0.8698122 1.6483605 2.2669841
## M19 2.5915144 1.8929559 1.2702366
## M20 4.6950094 0.0000000 1.5903775
## M21 1.9846711 0.0000000 0.8162694
## Maturing Excitatory Upper Enriched Excitatory Deep Layer 1
## M1 1.4851557 1.3901412
## M2 1.3677512 1.2884967
## M3 0.0000000 0.9340645
## M4 0.2506931 0.1164305
## M5 1.3819747 1.1657322
## M6 0.6181268 0.7289776
## M7 0.0000000 0.7289776
## M8 1.0864507 1.5667110
## M9 0.3724064 0.1729583
## M10 5.0774148 2.4613881
## M11 0.0000000 0.0000000
## M12 0.4144523 0.1924858
## M13 0.0000000 0.2131093
## M14 2.4168781 2.0549419
## M15 2.5945897 3.0223885
## M16 2.1226976 2.2891839
## M17 3.4969415 3.0553155
## M18 1.1699009 0.8193414
## M19 0.0000000 0.9425103
## M20 3.4735051 2.4491034
## M21 1.7548514 2.5565202
## Excitatory Deep Layer 2 Interneuron MGE Interneuron CGE
## M1 1.2585209 1.0480994 1.1708193
## M2 0.9603253 0.8083258 1.1028433
## M3 0.7493816 0.6155069 0.6219618
## M4 1.1396211 0.3565953 0.7299307
## M5 1.2097047 1.1631494 1.1754751
## M6 0.7747367 1.3407560 0.8911692
## M7 2.3008179 0.4351312 0.8911692
## M8 1.6333197 0.5041520 0.0000000
## M9 0.5815810 0.5297249 0.0000000
## M10 1.5935939 4.7023126 3.4623188
## M11 0.6378631 0.0000000 0.0000000
## M12 1.0576281 0.5895326 0.0000000
## M13 0.0000000 0.0000000 1.3387519
## M14 1.3368589 1.3308257 0.6624289
## M15 2.5046535 2.9434660 0.7102002
## M16 2.2175408 5.5929766 1.4927083
## M17 1.7883487 5.0530583 5.1083393
## M18 1.0998388 1.6691712 2.5713517
## M19 0.4080257 0.0000000 0.0000000
## M20 1.8856317 2.4019780 3.7094909
## M21 0.8074613 1.2286057 1.2413584
## Oligodendrocyte Precursor Cells Endothelial Pericyte Microglia
## M1 1.2568125 2.5551675 1.7284102 3.7709620
## M2 1.1186514 0.6218384 1.4673890 0.7337367
## M3 0.3519890 0.3521460 0.5072170 0.6039841
## M4 1.0685293 0.4989316 2.2445361 0.3738398
## M5 1.0143440 0.9949531 1.3961125 0.6942748
## M6 3.2706805 0.6104752 0.6630700 0.5255024
## M7 1.7354010 0.1990903 1.7016940 0.3904645
## M8 1.2037620 2.5745810 2.7793191 1.5373217
## M9 0.9370034 2.8690481 2.3066187 2.8299359
## M10 1.7841463 1.6940233 0.5614546 1.6270487
## M11 0.1653987 0.1320081 0.2572255 0.8922794
## M12 0.5113966 0.4076780 0.7173871 1.0037782
## M13 0.7613156 0.6065513 0.6928041 0.4887605
## M14 2.8925128 1.2535337 0.9061444 3.6070685
## M15 0.4039335 1.8944309 1.2065617 1.5669912
## M16 1.9918983 1.9638603 2.8468216 1.1292911
## M17 1.1465113 1.4959298 1.2113067 0.8298423
## M18 2.2613904 2.0126960 2.6134470 4.7038123
## M19 1.3970734 2.0773336 1.9808990 1.0136329
## M20 1.0311768 1.3997802 1.2799263 0.7097655
## M21 2.2274380 0.5652139 0.7403588 0.9350200# P-values
Pmat## Human-Specific Prenatal Human-Specific Early Postnatal Human-Specific Adult
## M1 0.6800617437 0.5955244482 0.6591629824
## M2 0.1960758849 0.1045839388 0.0067252802
## M3 0.4392757585 0.1330369600 0.9057246687
## M4 0.0032088251 0.1856949277 0.0005431457
## M5 0.9970581993 0.9852682016 0.9995259102
## M6 0.0075069866 0.0004168909 0.2480903154
## M7 0.0707195383 0.3129950040 0.0480985714
## M8 0.3512091945 0.2673982308 0.8748416686
## M9 0.2449379868 0.6917977289 0.0203965284
## M10 0.9998484417 0.9882672300 0.5388466068
## M11 0.1927812433 0.2648134036 0.0322256536
## M12 0.9995524542 0.9998815479 0.9976641975
## M13 0.0005631452 0.0508657465 0.0178827309
## M14 0.9437886873 0.9999723952 0.9147926877
## M15 0.8421521059 0.9905033619 0.9033504860
## M16 0.9041488876 0.9431953616 0.8131147626
## M17 0.4241894989 0.2518764123 0.3104227417
## M18 0.0573984660 0.0078015312 0.0370215321
## M19 0.3412339804 0.3881713086 0.2417597931
## M20 0.1897364144 0.0472099035 0.2593180182
## M21 0.0939662146 0.9520240818 0.0610342747
## Human-Accelerated Genes Human-Gained Enhancers Fetal
## M1 0.73479958 0.32474898
## M2 0.94183305 0.80921147
## M3 0.45137264 1.00000000
## M4 0.43672790 0.07155434
## M5 0.06760338 0.88144542
## M6 0.40768203 0.27458086
## M7 0.66494252 0.84256528
## M8 0.24066088 0.46883704
## M9 0.91453177 1.00000000
## M10 0.19256032 1.00000000
## M11 0.03721950 0.39851685
## M12 0.86164493 0.74378622
## M13 0.12902422 0.12228568
## M14 0.03380169 0.12110260
## M15 0.27522964 1.00000000
## M16 0.24608953 1.00000000
## M17 0.48477008 0.27235491
## M18 0.15917827 0.62337881
## M19 0.48824832 1.00000000
## M20 0.71713467 0.49370424
## M21 0.87274176 1.00000000
## Human-Gained Enhancers Adult Human-Lossed Enhancers PC1
## M1 0.06855740 0.352933165 5.935403e-02
## M2 0.74294087 0.828590520 4.197010e-07
## M3 0.70580818 0.993867013 1.000000e+00
## M4 0.56848230 0.356743105 5.324010e-10
## M5 0.59288067 0.857071024 1.021548e-01
## M6 0.62647865 0.270168000 9.943412e-01
## M7 0.62647865 0.987228640 1.479568e-03
## M8 0.88324072 0.002850052 8.417646e-02
## M9 0.05991156 0.811063602 9.995104e-01
## M10 0.10125943 0.052203150 5.180103e-08
## M11 0.87733387 0.013422784 9.248729e-02
## M12 0.99283286 0.984134653 9.423707e-01
## M13 0.09350787 0.041078396 2.238980e-07
## M14 0.15746888 0.319693592 3.820784e-08
## M15 0.59252176 0.661214775 1.798807e-01
## M16 0.26963494 0.073456196 1.440780e-01
## M17 0.89813636 0.555478913 9.690051e-01
## M18 0.42921018 0.346159424 1.293184e-01
## M19 0.17599722 0.932383930 5.926816e-01
## M20 0.93452940 0.991302208 1.584662e-02
## M21 0.79176126 0.940416477 1.223506e-03
## PC2 Song Bird DE ASD Prenatal1 ASD DE Downreg ASD DE Upreg
## M1 2.637668e-02 0.0686560913 1.398542e-01 0.98232330 1.145216e-08
## M2 3.328735e-06 0.7890537721 9.982193e-01 0.04977199 8.118114e-01
## M3 1.000000e+00 0.6994249314 2.350972e-01 0.56240419 9.980045e-01
## M4 2.305515e-15 0.6794663082 1.000000e+00 0.01149144 9.987997e-01
## M5 7.288405e-02 0.0202334860 9.999946e-01 0.16902285 9.612071e-01
## M6 9.862370e-01 0.9070402562 9.999999e-01 0.84029789 5.008939e-01
## M7 5.518348e-08 0.0538699892 1.000000e+00 0.01095065 9.917697e-01
## M8 2.213740e-01 0.2146392324 9.999971e-01 0.81780990 4.623392e-02
## M9 9.959586e-01 0.9828514517 9.997310e-01 0.99871903 3.569456e-23
## M10 3.401034e-09 0.0259093142 1.670203e-56 0.50999623 7.432790e-01
## M11 7.292545e-04 0.7292921559 9.330775e-01 0.40700054 9.773110e-01
## M12 7.594940e-01 0.9195037035 9.965574e-01 0.98643873 9.985442e-01
## M13 2.955952e-03 0.0079243992 2.272932e-09 0.17627071 2.530046e-01
## M14 1.050465e-06 0.0000112374 1.654781e-35 0.92710459 4.966855e-03
## M15 1.392085e-01 0.2434298084 3.937756e-08 0.06829568 4.362249e-01
## M16 5.363861e-01 0.6244182204 5.820073e-01 0.30839728 1.631384e-01
## M17 9.966840e-01 0.3028552337 9.354021e-01 0.24921897 6.417563e-01
## M18 3.294613e-02 0.9725057175 9.706677e-01 0.50042332 2.184503e-02
## M19 7.270054e-01 0.2776660490 9.908645e-01 0.55088177 2.954856e-01
## M20 1.189058e-03 0.0946815450 6.286830e-02 0.04474792 6.322903e-01
## M21 1.792612e-05 0.0034648597 1.186674e-03 0.01385257 2.358044e-01
## ASD CTX Downreg ASD CTX Upreg ASD 102 dnPTVs SFARI ASD FMRP Targets1
## M1 7.494915e-01 2.703829e-14 0.279122496 1.651599e-01 7.881637e-03
## M2 5.223048e-01 9.970632e-01 0.948230431 9.989496e-01 9.999985e-01
## M3 9.528273e-01 9.999843e-01 0.922337645 7.227227e-01 8.378774e-01
## M4 1.219902e-02 9.999268e-01 0.886216427 9.991411e-01 9.999965e-01
## M5 7.010504e-01 5.242958e-01 0.871284409 9.728649e-01 9.996640e-01
## M6 9.716902e-01 8.880005e-01 1.000000000 5.781938e-01 1.000000e+00
## M7 1.327226e-01 9.999018e-01 1.000000000 9.976541e-01 9.999966e-01
## M8 5.328847e-01 4.844016e-04 0.783969294 8.189307e-01 7.328757e-01
## M9 9.799242e-01 4.198647e-15 0.767273854 9.865858e-01 9.996203e-01
## M10 1.689401e-07 5.565337e-01 0.014378011 4.949348e-06 2.421287e-28
## M11 3.513307e-01 9.969872e-01 1.000000000 9.912119e-01 2.089060e-01
## M12 9.992646e-01 9.964836e-01 1.000000000 9.712608e-01 9.947823e-01
## M13 4.437961e-02 9.469345e-01 1.000000000 6.713513e-01 1.727605e-02
## M14 6.063645e-01 6.029478e-01 0.001081301 6.192454e-11 2.181783e-16
## M15 1.628549e-01 5.093376e-01 0.004620462 3.983006e-03 1.389209e-01
## M16 6.376186e-01 9.642949e-02 0.282855232 2.793186e-01 6.020409e-01
## M17 5.673962e-01 1.736198e-01 1.000000000 3.334170e-01 6.861658e-01
## M18 9.076579e-01 3.414855e-04 0.014068800 9.898798e-02 1.163827e-01
## M19 5.333978e-01 2.699597e-03 1.000000000 5.866381e-01 6.721075e-01
## M20 9.789871e-02 6.244737e-01 1.000000000 8.770810e-01 8.362937e-01
## M21 4.701197e-01 8.699811e-01 1.000000000 4.861942e-02 8.153461e-01
## FMRP Targets2 CHD8 Targets1 CHD8 Targets2 SCZ DE BD DE
## M1 4.095473e-02 3.167298e-02 9.999192e-01 2.857653e-10 0.054090368
## M2 9.712568e-01 1.588174e-01 2.804390e-08 5.794778e-01 0.004696712
## M3 1.947233e-01 9.983422e-01 9.999934e-01 9.999636e-01 0.980852330
## M4 9.982526e-01 7.557991e-01 1.793970e-14 9.850103e-01 0.974311013
## M5 8.419424e-01 4.175197e-04 5.013581e-11 7.929966e-01 0.995751021
## M6 2.487979e-01 9.894425e-01 4.013641e-04 6.941007e-01 0.929777629
## M7 9.650118e-01 5.934635e-02 2.966515e-28 9.885675e-01 0.982596922
## M8 3.300160e-02 9.960772e-01 1.000000e+00 8.892040e-02 0.297350652
## M9 7.929112e-01 9.940158e-01 9.999822e-01 1.835371e-06 0.002577891
## M10 1.256270e-01 1.708656e-12 5.246682e-07 8.484042e-03 0.921917747
## M11 5.340016e-01 6.168471e-04 3.518266e-05 6.169365e-01 0.146593318
## M12 5.214237e-01 2.371117e-01 1.940369e-02 9.971954e-01 0.299472698
## M13 7.284996e-03 3.915347e-05 2.611235e-09 1.236247e-03 0.284908585
## M14 2.960261e-07 1.254505e-25 9.122386e-15 3.907742e-01 0.932040081
## M15 2.097548e-01 3.316486e-05 1.872106e-07 7.965069e-01 0.702988219
## M16 5.918653e-01 9.760474e-01 9.999999e-01 1.295699e-02 0.529627619
## M17 7.826138e-01 9.985623e-01 1.000000e+00 4.736431e-02 0.019467736
## M18 2.860690e-01 9.867522e-01 9.999765e-01 2.687023e-04 0.261374913
## M19 6.861128e-01 9.995542e-01 1.000000e+00 1.237460e-02 0.041972988
## M20 2.929117e-01 4.310762e-03 3.801379e-05 3.521917e-02 0.916401245
## M21 5.439622e-01 2.373493e-06 2.871068e-11 3.120775e-01 0.431704507
## ASD Excitatory ASD Inhibitory ASD Microglia ASD Oligodendrocyte
## M1 0.97342895 0.90715505 0.01297764 0.80837359
## M2 0.16159166 0.11967822 0.64236463 0.40076730
## M3 0.90367784 0.79252380 1.00000000 1.00000000
## M4 0.84712088 0.71364679 0.47674531 1.00000000
## M5 1.00000000 0.41599342 1.00000000 1.00000000
## M6 1.00000000 1.00000000 1.00000000 1.00000000
## M7 0.75798712 0.87289162 1.00000000 1.00000000
## M8 0.20685253 0.52664401 0.68125996 1.00000000
## M9 0.89545422 0.81582510 0.66305435 1.00000000
## M10 0.89459008 0.23228663 0.29076669 1.00000000
## M11 0.60675607 0.78646887 0.62944014 1.00000000
## M12 1.00000000 0.78114538 0.25179783 0.20263332
## M13 1.00000000 0.74633570 0.58604654 1.00000000
## M14 0.83840382 0.74477220 0.01106741 1.00000000
## M15 0.50310159 0.03755666 1.00000000 1.00000000
## M16 0.22182748 0.70786062 1.00000000 1.00000000
## M17 0.45058891 0.31599313 1.00000000 0.01211158
## M18 0.05637159 0.02299887 0.03223266 1.00000000
## M19 0.71813169 1.00000000 0.12276259 0.13186173
## M20 0.63707996 0.53205664 0.38632203 1.00000000
## M21 1.00000000 0.51756259 0.37416693 0.10294288
## ASD Astrocyte ASD Endothelial Ventricular Radial Glia Outer Radial Glia
## M1 0.78871421 0.001420584 0.520064384 0.2085175030
## M2 1.00000000 1.000000000 0.014010556 0.9374048359
## M3 0.68549724 0.734757022 0.943554408 0.9883664650
## M4 1.00000000 1.000000000 0.929038462 0.8522166400
## M5 1.00000000 0.655177645 0.214668538 0.1046203022
## M6 1.00000000 0.602716198 0.002976379 0.0001467560
## M7 1.00000000 1.000000000 0.031093841 0.0009292945
## M8 1.00000000 0.043549545 0.156756094 0.0116410895
## M9 1.00000000 0.171941375 0.067448722 0.0631452064
## M10 0.48188344 0.170978020 0.334920703 0.1847170674
## M11 0.02137618 1.000000000 0.984365117 0.9403959600
## M12 1.00000000 0.145375721 0.380592703 0.3038835117
## M13 1.00000000 1.000000000 0.622935454 0.7899601492
## M14 0.09744577 0.457265839 0.783419835 0.3310714556
## M15 0.39150006 0.434424149 0.378503579 0.5739678927
## M16 0.08174720 1.000000000 0.206345051 0.8450874205
## M17 0.36167421 1.000000000 0.164520284 0.1024180275
## M18 0.06631134 0.385896486 0.421919039 0.0160476649
## M19 1.00000000 1.000000000 0.033009206 0.0060767063
## M20 1.00000000 1.000000000 0.002983325 0.0036155318
## M21 0.24748342 1.000000000 0.546360378 0.4658343846
## Cycling Progenitors S phase Cycling Progenitors G2M phase
## M1 1.000000e+00 9.772206e-01
## M2 1.187390e-02 9.880223e-01
## M3 4.708982e-01 9.836252e-01
## M4 3.289352e-03 3.405268e-01
## M5 5.847467e-01 3.865154e-01
## M6 8.273253e-68 4.750545e-72
## M7 1.966441e-10 7.944584e-09
## M8 9.957477e-01 9.377736e-01
## M9 9.785584e-01 8.303862e-01
## M10 2.669897e-01 6.978537e-01
## M11 9.975767e-01 9.985047e-01
## M12 5.627705e-02 9.883687e-02
## M13 5.709906e-02 2.072903e-01
## M14 9.758054e-01 9.782005e-01
## M15 6.598549e-01 7.751598e-01
## M16 9.893811e-01 9.943979e-01
## M17 8.571790e-01 7.042336e-01
## M18 9.796674e-01 8.321904e-01
## M19 3.793925e-01 7.560663e-01
## M20 2.093532e-01 2.071115e-01
## M21 1.459046e-02 3.194743e-02
## Intermediate Progenitors Migrating Excitatory Maturing Excitatory
## M1 9.676626e-01 0.5588953354 0.310484934
## M2 1.465173e-10 0.8608521663 0.001772467
## M3 1.747469e-01 0.8176966869 1.000000000
## M4 9.051170e-01 0.7648880499 0.797612181
## M5 1.322429e-01 0.3907990310 0.764524122
## M6 6.394335e-01 1.0000000000 0.455802946
## M7 4.542769e-03 0.3265913294 0.675845183
## M8 8.942081e-01 1.0000000000 0.079366900
## M9 1.000000e+00 1.0000000000 0.944106994
## M10 1.496511e-01 0.0703896646 0.540931734
## M11 9.642202e-01 1.0000000000 1.000000000
## M12 4.043874e-01 0.5819084853 0.924994474
## M13 1.000000e+00 0.5449445450 0.674960515
## M14 7.903194e-01 1.0000000000 0.198377578
## M15 1.000000e+00 0.5185420259 0.005802425
## M16 9.296479e-01 0.0006507171 0.017912669
## M17 2.302212e-01 0.4834205143 0.013305891
## M18 6.776376e-01 0.4649302383 0.112429252
## M19 5.250104e-02 0.4199062645 0.477254489
## M20 1.211356e-03 1.0000000000 0.367870723
## M21 2.039745e-01 1.0000000000 0.711375333
## Maturing Excitatory Upper Enriched Excitatory Deep Layer 1
## M1 1.593766e-01 0.136919768
## M2 2.968351e-01 0.258998195
## M3 1.000000e+00 0.664508587
## M4 9.828621e-01 0.999829213
## M5 3.245527e-01 0.405694618
## M6 8.424489e-01 0.827366080
## M7 1.000000e+00 0.827366080
## M8 5.391903e-01 0.147542266
## M9 9.346264e-01 0.997028776
## M10 1.568752e-05 0.004817954
## M11 1.000000e+00 1.000000000
## M12 9.136626e-01 0.994621762
## M13 1.000000e+00 0.991064466
## M14 6.724341e-02 0.042581955
## M15 5.301986e-02 0.001330026
## M16 1.335791e-01 0.024198991
## M17 1.031726e-02 0.001877423
## M18 5.206959e-01 0.716824234
## M19 1.000000e+00 0.626511453
## M20 3.346473e-02 0.044559292
## M21 3.250538e-01 0.037712309
## Excitatory Deep Layer 2 Interneuron MGE Interneuron CGE
## M1 0.2645068248 0.6458370261 0.508063057
## M2 0.6586995736 0.7414277730 0.526911472
## M3 0.8862674005 0.8480033325 0.843952263
## M4 0.3969054518 0.9433802876 0.773014557
## M5 0.3176993121 0.4999762206 0.493200857
## M6 0.8243958366 0.4085657803 0.671222783
## M7 0.0008423092 0.9044610324 0.671222783
## M8 0.0695005440 0.8679257434 1.000000000
## M9 0.9340704909 0.8542764369 1.000000000
## M10 0.0887014775 0.0006173896 0.011283632
## M11 0.8965720931 1.0000000000 1.000000000
## M12 0.5091596650 0.8226521550 1.000000000
## M13 1.0000000000 1.0000000000 0.453625316
## M14 0.2599209935 0.4565426981 0.785373313
## M15 0.0022198616 0.0556965528 0.761896171
## M16 0.0103478520 0.0004772218 0.399887148
## M17 0.0699212461 0.0019098581 0.001813483
## M18 0.4810479813 0.3483938531 0.122324849
## M19 0.9566361952 1.0000000000 1.000000000
## M20 0.0937054228 0.2117077322 0.053369319
## M21 0.7233754722 0.5638595436 0.560175925
## Oligodendrocyte Precursor Cells Endothelial Pericyte Microglia
## M1 2.968858e-01 1.828622e-10 1.487768e-04 4.428662e-34
## M2 4.369907e-01 9.671023e-01 4.149281e-02 9.457741e-01
## M3 9.968134e-01 9.986846e-01 9.965668e-01 9.846914e-01
## M4 5.008481e-01 9.824204e-01 4.290634e-05 9.994338e-01
## M5 5.634468e-01 5.855947e-01 1.042687e-01 9.348431e-01
## M6 4.925106e-06 9.325091e-01 9.394721e-01 9.855365e-01
## M7 5.557036e-02 9.995432e-01 1.708225e-02 9.979568e-01
## M8 3.766190e-01 3.697211e-04 2.539775e-06 6.455009e-02
## M9 6.370287e-01 6.756624e-05 3.030586e-04 2.518760e-06
## M10 6.188938e-02 6.265407e-02 9.667845e-01 4.200887e-02
## M11 9.976890e-01 9.994888e-01 9.992866e-01 7.016842e-01
## M12 9.348993e-01 9.782941e-01 8.741746e-01 5.621961e-01
## M13 7.786223e-01 8.989475e-01 8.821227e-01 9.753843e-01
## M14 8.569232e-04 3.333974e-01 6.776287e-01 2.131041e-08
## M15 9.591354e-01 4.373000e-02 3.348670e-01 8.697679e-02
## M16 4.942732e-02 3.556386e-02 3.740682e-05 4.187163e-01
## M17 4.582620e-01 1.900954e-01 3.401420e-01 7.477263e-01
## M18 2.572225e-02 3.783806e-02 4.283035e-04 7.663365e-11
## M19 3.050425e-01 3.992549e-02 2.304611e-02 5.523216e-01
## M20 5.661117e-01 3.030944e-01 3.275286e-01 8.167294e-01
## M21 6.376218e-02 8.701598e-01 7.912346e-01 6.284497e-01# FDR
FDRmat## Human-Specific Prenatal Human-Specific Early Postnatal Human-Specific Adult
## M1 0.95208644 0.962001032 0.98874447
## M2 0.45751040 0.439252543 0.07061544
## M3 0.65891364 0.465629360 0.99952591
## M4 0.03369266 0.557084783 0.01140606
## M5 0.99984844 0.999972395 0.99952591
## M6 0.05254891 0.008754709 0.49506167
## M7 0.29702206 0.597535917 0.14429571
## M8 0.61461609 0.561536285 0.99952591
## M9 0.51436977 0.999972395 0.10708177
## M10 0.99984844 0.999972395 0.87044452
## M11 0.45751040 0.561536285 0.12957536
## M12 0.99984844 0.999972395 0.99952591
## M13 0.01182605 0.267045169 0.10708177
## M14 0.99984844 0.999972395 0.99952591
## M15 0.99984844 0.999972395 0.99952591
## M16 0.99984844 0.999972395 0.99952591
## M17 0.65891364 0.561536285 0.54323980
## M18 0.29702206 0.081916077 0.12957536
## M19 0.61461609 0.679299790 0.49506167
## M20 0.45751040 0.267045169 0.49506167
## M21 0.32888175 0.999972395 0.16021497
## Human-Accelerated Genes Human-Gained Enhancers Fetal
## M1 0.9076936 1.0000000
## M2 0.9418330 1.0000000
## M3 0.7323725 1.0000000
## M4 0.7323725 0.8559998
## M5 0.4732236 1.0000000
## M6 0.7323725 1.0000000
## M7 0.9076936 1.0000000
## M8 0.6422025 1.0000000
## M9 0.9418330 1.0000000
## M10 0.6422025 1.0000000
## M11 0.3908048 1.0000000
## M12 0.9418330 1.0000000
## M13 0.6422025 0.8559998
## M14 0.3908048 0.8559998
## M15 0.6422025 1.0000000
## M16 0.6422025 1.0000000
## M17 0.7323725 1.0000000
## M18 0.6422025 1.0000000
## M19 0.7323725 1.0000000
## M20 0.9076936 1.0000000
## M21 0.9418330 1.0000000
## Human-Gained Enhancers Adult Human-Lossed Enhancers PC1
## M1 0.5316120 0.74916052 1.384927e-01
## M2 0.9812559 0.99386701 1.762744e-06
## M3 0.9812559 0.99386701 1.000000e+00
## M4 0.9812559 0.74916052 1.118042e-08
## M5 0.9812559 0.99386701 1.787709e-01
## M6 0.9812559 0.74916052 1.000000e+00
## M7 0.9812559 0.99386701 4.438704e-03
## M8 0.9812559 0.05985109 1.765666e-01
## M9 0.5316120 0.99386701 1.000000e+00
## M10 0.5316120 0.27406654 3.626072e-07
## M11 0.9812559 0.14093923 1.765666e-01
## M12 0.9928329 0.99386701 1.000000e+00
## M13 0.5316120 0.27406654 1.175465e-06
## M14 0.6159903 0.74916052 3.626072e-07
## M15 0.9812559 0.99386701 2.518330e-01
## M16 0.8089048 0.30851602 2.161170e-01
## M17 0.9812559 0.99386701 1.000000e+00
## M18 0.9812559 0.74916052 2.088990e-01
## M19 0.6159903 0.99386701 7.778946e-01
## M20 0.9812559 0.99386701 4.159739e-02
## M21 0.9812559 0.99386701 4.282271e-03
## PC2 Song Bird DE ASD Prenatal1 ASD DE Downreg ASD DE Upreg
## M1 5.539104e-02 0.2059682739 4.195625e-01 0.99871903 1.202476e-07
## M2 1.398069e-05 0.9747134832 1.000000e+00 0.20904237 9.987997e-01
## M3 1.000000e+00 0.9571959546 6.171302e-01 0.78736587 9.987997e-01
## M4 4.841582e-14 0.9571959546 1.000000e+00 0.09696799 9.987997e-01
## M5 1.275471e-01 0.1062258014 1.000000e+00 0.46271061 9.987997e-01
## M6 1.000000e+00 0.9828514517 1.000000e+00 0.99871903 9.562520e-01
## M7 3.862844e-07 0.1885449622 1.000000e+00 0.09696799 9.987997e-01
## M8 3.320609e-01 0.5008248755 1.000000e+00 0.99871903 1.941825e-01
## M9 1.000000e+00 0.9828514517 1.000000e+00 0.99871903 7.495859e-22
## M10 3.571086e-08 0.1088191197 3.507427e-55 0.78736587 9.987997e-01
## M11 2.187763e-03 0.9571959546 1.000000e+00 0.77700104 9.987997e-01
## M12 9.381984e-01 0.9828514517 1.000000e+00 0.99871903 9.987997e-01
## M13 6.897221e-03 0.0554707944 1.591052e-08 0.46271061 6.641370e-01
## M14 5.514942e-06 0.0002359855 1.737520e-34 0.99871903 3.476798e-02
## M15 2.248753e-01 0.5112025977 2.067322e-07 0.23903489 9.160723e-01
## M16 7.509406e-01 0.9571959546 1.000000e+00 0.64763429 5.709845e-01
## M17 1.000000e+00 0.5299966590 1.000000e+00 0.58151093 9.987997e-01
## M18 6.289716e-02 0.9828514517 1.000000e+00 0.78736587 1.146864e-01
## M19 9.381984e-01 0.5299966590 1.000000e+00 0.78736587 6.894664e-01
## M20 3.121278e-03 0.2485390557 2.200391e-01 0.20904237 9.987997e-01
## M21 6.274142e-05 0.0363810271 4.984030e-03 0.09696799 6.641370e-01
## ASD CTX Downreg ASD CTX Upreg ASD 102 dnPTVs SFARI ASD FMRP Targets1
## M1 9.837076e-01 2.839020e-13 0.98999331 5.780598e-01 5.517146e-02
## M2 9.564279e-01 9.999843e-01 1.00000000 9.991411e-01 1.000000e+00
## M3 9.992646e-01 9.999843e-01 1.00000000 9.991411e-01 1.000000e+00
## M4 1.280897e-01 9.999843e-01 1.00000000 9.991411e-01 1.000000e+00
## M5 9.814706e-01 9.999843e-01 1.00000000 9.991411e-01 1.000000e+00
## M6 9.992646e-01 9.999843e-01 1.00000000 9.991411e-01 1.000000e+00
## M7 5.574350e-01 9.999843e-01 1.00000000 9.991411e-01 1.000000e+00
## M8 9.564279e-01 2.543109e-03 1.00000000 9.991411e-01 1.000000e+00
## M9 9.992646e-01 8.817158e-14 1.00000000 9.991411e-01 1.000000e+00
## M10 3.547741e-06 9.999843e-01 0.07548456 5.196816e-05 5.084702e-27
## M11 9.564279e-01 9.999843e-01 1.00000000 9.991411e-01 6.267180e-01
## M12 9.992646e-01 9.999843e-01 1.00000000 9.991411e-01 1.000000e+00
## M13 3.106572e-01 9.999843e-01 1.00000000 9.991411e-01 9.069926e-02
## M14 9.564279e-01 9.999843e-01 0.02270732 1.300415e-09 2.290873e-15
## M15 5.699920e-01 9.999843e-01 0.04851485 2.788104e-02 4.862231e-01
## M16 9.564279e-01 3.375032e-01 0.98999331 8.379558e-01 1.000000e+00
## M17 9.564279e-01 5.208595e-01 1.00000000 8.752196e-01 1.000000e+00
## M18 9.992646e-01 2.390399e-03 0.07548456 4.157495e-01 4.862231e-01
## M19 9.564279e-01 1.133831e-02 1.00000000 9.991411e-01 1.000000e+00
## M20 5.139682e-01 9.999843e-01 1.00000000 9.991411e-01 1.000000e+00
## M21 9.564279e-01 9.999843e-01 1.00000000 2.552520e-01 1.000000e+00
## FMRP Targets2 CHD8 Targets1 CHD8 Targets2 SCZ DE BD DE
## M1 2.150123e-01 7.390363e-02 1.000000e+00 6.001072e-09 0.22717955
## M2 9.982526e-01 3.031969e-01 8.413170e-08 9.254047e-01 0.04931547
## M3 6.151146e-01 9.995542e-01 1.000000e+00 9.999636e-01 0.99575102
## M4 9.982526e-01 9.995542e-01 1.255779e-13 9.999636e-01 0.99575102
## M5 9.822661e-01 1.461319e-03 2.105704e-10 9.839203e-01 0.99575102
## M6 6.151146e-01 9.995542e-01 7.023872e-04 9.717410e-01 0.99575102
## M7 9.982526e-01 1.246273e-01 6.229682e-27 9.999636e-01 0.99575102
## M8 2.150123e-01 9.995542e-01 1.000000e+00 1.867328e-01 0.62889267
## M9 9.794786e-01 9.995542e-01 1.000000e+00 1.927139e-05 0.04931547
## M10 5.276336e-01 1.794088e-11 1.224226e-06 3.563298e-02 0.99575102
## M11 8.787081e-01 1.850541e-03 7.257177e-05 9.254047e-01 0.51307661
## M12 8.787081e-01 4.149456e-01 3.134443e-02 9.999636e-01 0.62889267
## M13 7.649245e-02 1.644446e-04 9.139324e-09 6.490296e-03 0.62889267
## M14 6.216549e-06 2.634461e-24 9.578505e-14 6.838548e-01 0.99575102
## M15 6.151146e-01 1.644446e-04 4.914278e-07 9.839203e-01 0.99575102
## M16 8.877979e-01 9.995542e-01 1.000000e+00 3.887096e-02 0.92684833
## M17 9.794786e-01 9.995542e-01 1.000000e+00 1.105167e-01 0.13627415
## M18 6.151146e-01 9.995542e-01 1.000000e+00 1.880916e-03 0.62889267
## M19 9.605579e-01 9.995542e-01 1.000000e+00 3.887096e-02 0.22035819
## M20 6.151146e-01 1.131575e-02 7.257177e-05 9.245033e-02 0.99575102
## M21 8.787081e-01 1.661445e-05 1.507311e-10 5.957843e-01 0.82416315
## ASD Excitatory ASD Inhibitory ASD Microglia ASD Oligodendrocyte
## M1 1 1.0000000 0.1362652 1.0000000
## M2 1 0.8377476 1.0000000 1.0000000
## M3 1 1.0000000 1.0000000 1.0000000
## M4 1 1.0000000 1.0000000 1.0000000
## M5 1 1.0000000 1.0000000 1.0000000
## M6 1 1.0000000 1.0000000 1.0000000
## M7 1 1.0000000 1.0000000 1.0000000
## M8 1 1.0000000 1.0000000 1.0000000
## M9 1 1.0000000 1.0000000 1.0000000
## M10 1 1.0000000 1.0000000 1.0000000
## M11 1 1.0000000 1.0000000 1.0000000
## M12 1 1.0000000 1.0000000 1.0000000
## M13 1 1.0000000 1.0000000 1.0000000
## M14 1 1.0000000 0.1362652 1.0000000
## M15 1 0.3943449 1.0000000 1.0000000
## M16 1 1.0000000 1.0000000 1.0000000
## M17 1 1.0000000 1.0000000 0.2543432
## M18 1 0.3943449 0.2256286 1.0000000
## M19 1 1.0000000 0.6445036 0.9230321
## M20 1 1.0000000 1.0000000 1.0000000
## M21 1 1.0000000 1.0000000 0.9230321
## ASD Astrocyte ASD Endothelial Ventricular Radial Glia Outer Radial Glia
## M1 1.0000000 0.02983226 0.71709800 0.398078869
## M2 1.0000000 1.00000000 0.09807389 0.987415758
## M3 1.0000000 1.00000000 0.98436512 0.988366465
## M4 1.0000000 1.00000000 0.98436512 0.987415758
## M5 1.0000000 1.00000000 0.45080393 0.244114038
## M6 1.0000000 1.00000000 0.03132491 0.003081876
## M7 1.0000000 1.00000000 0.13863866 0.009757592
## M8 1.0000000 0.45727022 0.43186575 0.048892576
## M9 1.0000000 0.72215378 0.23607053 0.189435619
## M10 1.0000000 0.72215378 0.61480360 0.387905842
## M11 0.4488998 1.00000000 0.98436512 0.987415758
## M12 1.0000000 0.72215378 0.61480360 0.531796145
## M13 1.0000000 1.00000000 0.76950850 0.987415758
## M14 0.5115903 1.00000000 0.91398981 0.534807736
## M15 1.0000000 1.00000000 0.61480360 0.803555050
## M16 0.5115903 1.00000000 0.45080393 0.987415758
## M17 1.0000000 1.00000000 0.43186575 0.244114038
## M18 0.5115903 1.00000000 0.63287856 0.056166827
## M19 1.0000000 1.00000000 0.13863866 0.031902708
## M20 1.0000000 1.00000000 0.03132491 0.025308722
## M21 1.0000000 1.00000000 0.71709800 0.698751577
## Cycling Progenitors S phase Cycling Progenitors G2M phase
## M1 1.000000e+00 9.985047e-01
## M2 6.127993e-02 9.985047e-01
## M3 8.989874e-01 9.985047e-01
## M4 2.302547e-02 9.985047e-01
## M5 1.000000e+00 9.985047e-01
## M6 1.737383e-66 9.976144e-71
## M7 2.064764e-09 8.341813e-08
## M8 1.000000e+00 9.985047e-01
## M9 1.000000e+00 9.985047e-01
## M10 6.229761e-01 9.985047e-01
## M11 1.000000e+00 9.985047e-01
## M12 1.712972e-01 5.188935e-01
## M13 1.712972e-01 7.255159e-01
## M14 1.000000e+00 9.985047e-01
## M15 1.000000e+00 9.985047e-01
## M16 1.000000e+00 9.985047e-01
## M17 1.000000e+00 9.985047e-01
## M18 1.000000e+00 9.985047e-01
## M19 7.967243e-01 9.985047e-01
## M20 5.495522e-01 7.255159e-01
## M21 6.127993e-02 2.236320e-01
## Intermediate Progenitors Migrating Excitatory Maturing Excitatory
## M1 1.000000e+00 1.00000000 0.81502295
## M2 3.076863e-09 1.00000000 0.03722181
## M3 5.242407e-01 1.00000000 1.00000000
## M4 1.000000e+00 1.00000000 0.98528564
## M5 5.237789e-01 1.00000000 0.98528564
## M6 1.000000e+00 1.00000000 0.91112221
## M7 3.179938e-02 1.00000000 0.98528564
## M8 1.000000e+00 1.00000000 0.33334098
## M9 1.000000e+00 1.00000000 1.00000000
## M10 5.237789e-01 0.73909148 0.94663053
## M11 1.000000e+00 1.00000000 1.00000000
## M12 8.492135e-01 1.00000000 1.00000000
## M13 1.000000e+00 1.00000000 0.98528564
## M14 1.000000e+00 1.00000000 0.59513273
## M15 1.000000e+00 1.00000000 0.06092546
## M16 1.000000e+00 0.01366506 0.09404151
## M17 5.371828e-01 1.00000000 0.09314124
## M18 1.000000e+00 1.00000000 0.39350238
## M19 2.756304e-01 1.00000000 0.91112221
## M20 1.271924e-02 1.00000000 0.85836502
## M21 5.354329e-01 1.00000000 0.98528564
## Maturing Excitatory Upper Enriched Excitatory Deep Layer 1
## M1 0.4781297710 0.34426529
## M2 0.6826129514 0.54389621
## M3 1.0000000000 1.00000000
## M4 1.0000000000 1.00000000
## M5 0.6826129514 0.77450791
## M6 1.0000000000 1.00000000
## M7 1.0000000000 1.00000000
## M8 0.9435831108 0.34426529
## M9 1.0000000000 1.00000000
## M10 0.0003294379 0.03372568
## M11 1.0000000000 1.00000000
## M12 1.0000000000 1.00000000
## M13 1.0000000000 1.00000000
## M14 0.2824223225 0.13367788
## M15 0.2783542766 0.01971294
## M16 0.4675268880 0.12704470
## M17 0.1083312383 0.01971294
## M18 0.9435831108 1.00000000
## M19 1.0000000000 1.00000000
## M20 0.2342530915 0.13367788
## M21 0.6826129514 0.13367788
## Excitatory Deep Layer 2 Interneuron MGE Interneuron CGE
## M1 0.61718259 1.000000000 1.00000000
## M2 0.98804936 1.000000000 1.00000000
## M3 1.00000000 1.000000000 1.00000000
## M4 0.75772859 1.000000000 1.00000000
## M5 0.66716856 1.000000000 1.00000000
## M6 1.00000000 1.000000000 1.00000000
## M7 0.01768849 1.000000000 1.00000000
## M8 0.28111627 1.000000000 1.00000000
## M9 1.00000000 1.000000000 1.00000000
## M10 0.28111627 0.006482591 0.11847813
## M11 1.00000000 1.000000000 1.00000000
## M12 0.82248869 1.000000000 1.00000000
## M13 1.00000000 1.000000000 1.00000000
## M14 0.61718259 1.000000000 1.00000000
## M15 0.02330855 0.292406902 1.00000000
## M16 0.07243496 0.006482591 1.00000000
## M17 0.28111627 0.013369006 0.03808314
## M18 0.82248869 1.000000000 0.64220546
## M19 1.00000000 1.000000000 1.00000000
## M20 0.28111627 0.889172475 0.37358523
## M21 1.00000000 1.000000000 1.00000000
## Oligodendrocyte Precursor Cells Endothelial Pericyte Microglia
## M1 0.7117658939 3.840107e-09 7.810782e-04 9.300190e-33
## M2 0.7925563476 9.995432e-01 9.681655e-02 9.994338e-01
## M3 0.9976890346 9.995432e-01 9.992866e-01 9.994338e-01
## M4 0.7925563476 9.995432e-01 3.003444e-04 9.994338e-01
## M5 0.7925563476 9.995432e-01 2.189643e-01 9.994338e-01
## M6 0.0001034272 9.995432e-01 9.992866e-01 9.994338e-01
## M7 0.1912865305 9.995432e-01 5.124674e-02 9.994338e-01
## M8 0.7908999203 2.588048e-03 5.333527e-05 2.259253e-01
## M9 0.8361001046 7.094455e-04 1.272846e-03 1.322349e-05
## M10 0.1912865305 1.644669e-01 9.992866e-01 1.764373e-01
## M11 0.9976890346 9.995432e-01 9.992866e-01 9.994338e-01
## M12 0.9976890346 9.995432e-01 9.992866e-01 9.994338e-01
## M13 0.9618276037 9.995432e-01 9.992866e-01 9.994338e-01
## M14 0.0089976932 6.364860e-01 9.992866e-01 1.491729e-07
## M15 0.9976890346 1.311900e-01 5.494601e-01 2.609304e-01
## M16 0.1912865305 1.311900e-01 3.003444e-04 9.994338e-01
## M17 0.7925563476 4.435559e-01 5.494601e-01 9.994338e-01
## M18 0.1800557181 1.311900e-01 1.499062e-03 8.046533e-10
## M19 0.7117658939 1.311900e-01 6.049603e-02 9.994338e-01
## M20 0.7925563476 6.364860e-01 5.494601e-01 9.994338e-01
## M21 0.1912865305 9.995432e-01 9.992866e-01 9.994338e-01Find consensus genes
SA LV1 non-zero & Prenatal Progenitor & Prenatal PC1 (A-P) & ASD Prenatal CoExpMod
progenitor_list = unique(c(vRG,oRG,PgS,PgG2M,IP))
progen_pc1_list = progenitor_list[is.element(progenitor_list,W234_PC1)]
progen_pc1_asdprenatal_list = progen_pc1_list[is.element(progen_pc1_list,ASDPrenatal1)]
progen_pc1_asdprenatal_salv1_list = progen_pc1_asdprenatal_list[is.element(progen_pc1_asdprenatal_list,nz_genes)]
sort(progen_pc1_asdprenatal_salv1_list)## [1] "BAZ1B" "BAZ2B" "BRD8" "CCND2" "CHD4" "CKAP5" "DDX17"
## [8] "ILF3" "IRF2BP2" "KIF1B" "LMNB2" "MCM7" "NASP" "PARP1"
## [15] "PRKDC" "SEL1L3" "SON" "SOX4" "SRRM2" "SYNE2" "TLE1"
## [22] "TROVE2"# overlap with SFARI ASD genes
progen_pc1_asdprenatal_SFARI_salv1_list = progen_pc1_asdprenatal_salv1_list[is.element(progen_pc1_asdprenatal_salv1_list,SFARIASD)]
sort(progen_pc1_asdprenatal_SFARI_salv1_list)## [1] "BAZ2B" "PRKDC" "SON"SA LV1 non-zero & Human-specific & Prenatal Progenitor & Prenatal PC1 (A-P) & ASD Prenatal CoExpMod
hs_list = unique(c(HumanSpecific_Prenatal_Zhu,
HumanSpecific_EarlyPostnatal_Zhu,
HumanSpecific_Adult_Zhu))
progenitor_list = unique(c(vRG,oRG,PgS,PgG2M,IP))
hs_progen_list = hs_list[is.element(hs_list,progenitor_list)]
hs_progen_pc1_list = hs_progen_list[is.element(hs_progen_list,W234_PC1)]
hs_progen_pc1_asdprenatal_list = hs_progen_pc1_list[is.element(hs_progen_pc1_list,ASDPrenatal1)]
# hs_progen_pc1_asdprenatal_vocal_list = hs_progen_pc1_asdprenatal_list[is.element(hs_progen_pc1_asdprenatal_list,SongBirdDE)]
hs_progen_pc1_asdprenatal_salv1_list = hs_progen_pc1_asdprenatal_list[is.element(hs_progen_pc1_asdprenatal_list,nz_genes)]
sort(hs_progen_pc1_asdprenatal_salv1_list)## [1] "BRD8" "CCND2" "DDX17" "MCM7" "PARP1" "PRKDC" "SEL1L3" "SRRM2"# overlap with SFARI ASD genes
hs_progen_pc1_asdprenatal_SFARI_salv1_list = hs_progen_pc1_asdprenatal_salv1_list[is.element(hs_progen_pc1_asdprenatal_salv1_list,SFARIASD)]
sort(hs_progen_pc1_asdprenatal_SFARI_salv1_list)## [1] "PRKDC"SA LV1 non-zero & Human-specific & Vocal Learning
hs_list = unique(c(HumanSpecific_Prenatal_Zhu,
HumanSpecific_EarlyPostnatal_Zhu,
HumanSpecific_Adult_Zhu))
hs_vocal_list = hs_list[is.element(hs_list,SongBirdDE)]
hs_vocal_salv1_list = hs_vocal_list[is.element(hs_vocal_list,nz_genes)]
sort(hs_vocal_salv1_list)## [1] "ACOT7" "AKR1B1" "ANP32B" "AP3M2" "ARGLU1" "ATIC" "BACH1"
## [8] "BRI3" "BRWD3" "BTAF1" "CEP63" "CHD9" "CHUK" "CORO1C"
## [15] "CYB5A" "DDX18" "DMXL2" "DPP7" "DUSP5" "EIF3I" "FAR1"
## [22] "FBXO21" "FCHSD2" "FEM1C" "GNL3" "GSPT1" "H3F3A" "HARS"
## [29] "HEATR1" "HEBP2" "HSPE1" "IQGAP1" "JARID2" "KCTD21" "KLHDC4"
## [36] "MAP3K3" "MAPK14" "MED12" "MPV17" "MRPS6" "NAB1" "NCOA2"
## [43] "NDUFS5" "NIT2" "NOL6" "NSMAF" "OGT" "PARD6A" "POLD3"
## [50] "POLR2D" "PRKRIP1" "PSME4" "PSMF1" "RAD51C" "RAD54L" "RALGAPB"
## [57] "RNF126" "RNF19B" "RRP15" "SAAL1" "SETMAR" "SLC7A6" "SPG7"
## [64] "SSB" "STIL" "STRA13" "STRAP" "TAGLN2" "TDG" "TET2"
## [71] "TMEM5" "TOMM7" "UCP2" "VPS8" "WDR7" "WDYHV1" "XBP1"
## [78] "YARS"# overlap with SFARI ASD genes
hs_vocal_SFARI_salv1_list = hs_vocal_salv1_list[is.element(hs_vocal_salv1_list,SFARIASD)]
sort(hs_vocal_SFARI_salv1_list)## [1] "BRWD3" "BTAF1" "DMXL2" "JARID2" "RALGAPB" "TET2"